cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21989/d9/azvrn4
title: Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling

description: &lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; &lt;p&gt;
This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitant peripheral artery disease and diabetes mellitus.  
  
&lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; &lt;p&gt;
Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus.  Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models.  
  
&lt;p&gt;&lt;b&gt;Results:&lt;/b&gt;&lt;p&gt; 
There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n=45,971) with foot exams, 43% (n=38,393) had vascular imaging, and 50% (n=44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched between the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85+ (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84, VI = 88.76) and reintervention (t = 34.40, VI = 81.22) both models indicate age is most predictive.   
  
&lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt;&lt;p&gt;
 The use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased surveillance and intervention to improve outcomes for patients. Random forests are incredibly high performing models with difficult interpretation most ideally suited for times when accurate prediction is most desirable and can be used in tandem with more common approaches to provide a more thorough analysis of observational data.
DOI: 10.21989/d9/azvrn4/ua8onz
title: rf_ulcer_reint_roc.R
DOI: 10.21989/d9/azvrn4/klnr5k
title: analysis_ulcer_dead_March2020.R
DOI: 10.21989/d9/azvrn4/aeub0q
title: 1_Austin_PAD-DM_README.txt
DOI: 10.21989/d9/azvrn4/plotqo
title: analysis_ulcer_amp_March20200.R
DOI: 10.21989/d9/azvrn4/fkxcgh
title: analysis_ulcer_reint_March2020.R
DOI: 10.21989/d9/azvrn4/c0xlzj
title: Codes for Extract_analyst_version_final.tab
DOI: 10.21989/d9/azvrn4/jsgmhv
title: analysis_ulcer_reint_McFadden.R
DOI: 10.5281/zenodo.14651894
title: Nitrogen Input Effects on Halomethane Fluxes via Microorganism-Mediated Pathways in Temperate Salt Marsh Soil

description: 该数据集包括不同氮素输入梯度下 CH3Cl 和 CH3Br 的净发射通量,以及土壤理化性质和微生物数据。
DOI: 10.5281/zenodo.14651895
title: Nitrogen Input Effects on Halomethane Fluxes via Microorganism-Mediated Pathways in Temperate Salt Marsh Soil

description: 该数据集包括不同氮素输入梯度下 CH3Cl 和 CH3Br 的净发射通量,以及土壤理化性质和微生物数据。
DOI: 10.21989/d9/azvrn4/pa39iw
title: analysis_ulcer_dead_McFadden.R
DOI: 10.21989/d9/azvrn4/eqekgf
title: rf_ulcer_dead_roc.R
DOI: 10.21989/d9/azvrn4/xwglsk
title: rf_ulcer_amp_roc.R
DOI: 10.15468/dl.qsq4je
title: Occurrence Download

description: A dataset containing 316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Canis lupus Linnaeus, 1758"
 ]
}

 The dataset includes 316 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070685-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21989/d9/azvrn4/tipolu
title: analysis_ulcer_amp_McFadden.R
DOI: 10.5281/zenodo.14651996
title: texensis

description: Full set of scoring sheets for male-male interactions with Gryllus texensis individuals
DOI: 10.5281/zenodo.14651997
title: texensis

description: Full set of scoring sheets for male-male interactions with Gryllus texensis individuals
DOI: 10.21989/d9/azvrn4/3r2vur
title: Austin_MachineLearning_BMC_s12874-022-01774-8.pdf
DOI: 10.21989/d9/azvrn4/dtc3sj
title: ExpOutcomeCodes_cleaned_final.tab
DOI: 10.21989/d9/azvrn4/1toxkw
title: cohort_tables_30oct19.sas
DOI: 10.21989/d9/azvrn4/onjuk9
title: cohort_tables_22oct19.sas
DOI: 10.17632/sdxvcjby5d.1
title: supplementary data for baicalin

description: These data are the raw data used to generate the pictures
DOI: 10.21989/d9/azvrn4/8tbawl
title: build_cohort_1year.sas
DOI: 10.17632/sdxvcjby5d.2
title: supplementary data for baicalin

description: These data are the raw data used to generate the pictures
DOI: 10.21989/d9/wg34vo
title: Medical and Surgical Discharge Rates (1992-2015)

description: &lt;h2&gt;Overview&lt;/h2&gt;
&lt;p&gt;The Dartmouth Institute for Health Policy and Clinical Practice (TDI) has created a publicly available source of data that provides researchers, payers, regulators, and innovators with metrics that quantify temporal and regional patterns of health care spending and utilization in the United States. Using CMS Medicare claims data (mostly for age &gt;64 enrollees), Atlas researchers built cohorts (“denominators”) and numerous measures or events (“numerators”) which were then used to calculate rates either by geography or for patients assigned to specific hospitals. These rates, which are calculated consistently across time and place, provide researchers with opportunities to evaluate spatial and temporal variation/trends. &lt;/p&gt;

&lt;p&gt;This entry contains a wide variety of medical and surgical discharge rates for fee-for-service (FFS) Medicare beneficiaries, age 65 and older, discharged from hospitals for medical and surgical conditions. Examples of rates include discharges for hypertension, asthma, COPD, diabetes, gastroenteritis, hip fracture, back surgery, hip replacement, radical prostatectomy, and valve replacement. Rates are provided at the state, county, hospital referral region (HRR), and hospital service area (HSA) levels, and all rates have been adjusted for age, sex, and race. For the 2008-2014 period, the rates provided for each geographic area include overall annual rates, rates stratified by race (black and non-black), and rates stratified by gender (male and female). For 1992-2007 and 2015, only the overall annual rates are provided.&lt;/p&gt;

&lt;p&gt;In addition, this entry provides national and region-specific (nine regions) rate data for FFS Medicare beneficiaries for treatments related to eight different “preference-sensitive conditions”: early-stage breast cancer, stable angina, low back pain, arthritis of the knee or hip, carotid artery disease, gallstones, enlarged prostate, and early-stage prostate cancer. Ten of the treatment rates involve inpatient surgery, and one focuses on PSA testing. Rates are provided at the state, HRR, and HSA levels, and all rates have been adjusted for age, sex, and race as appropriate. The national rates are based on 2003-2008 data, while rates for the nine regions are based on 2008-2010 data.&lt;p&gt; 

&lt;p&gt; Users downloading data should review the methods sections of the related publications for context as well as for information about any temporal changes in methods. All reports in the Dartmouth Atlas of Health Care series are available from the National Library of Medicine
&lt;a href="https://www.ncbi.nlm.nih.gov/books/NBK584737/"&gt;https://www.ncbi.nlm.nih.gov/books/NBK584737/&lt;/a&gt;
&lt;p&gt;

&lt;p&gt; Note that for the general Dartmouth Atlas rate datasets, which span multiple decades, the author list includes all Dartmouth staff (programmers, analysts, supervisors, etc.) involved in generating all types of Atlas rates across all years. We do not attempt to assign individuals to specific datasets or years. &lt;p&gt;
DOI: 10.17632/m7zwpr2c6y.1
title: Longcovid questionniare_Thai

description: Symptoms based structured questionnaire to determine longcovid condition in Thai language
DOI: 10.17632/ndg6gtcf86.1
title: Spatial distribution of forest aboveground carbon storage in Liaoning Province in 2022

description: This repository contains the results for the paper entitled:Forest aboveground carbon storage estimation and uncertainty analysis by coupled multi-source remote sensing data in Liaoning province.
The dataset includes carbon density and uncertainty result across Liaoning province with a spatial resolution at 30 m.
Projection: EPSG:4326 GCS_WGS_1984
Predictors variables: Sentinel-2、Sentinel-1、SRTM
Spatial resolution: 30 m
DOI: 10.5281/zenodo.14651972
title: TALABALARNING O'QUV FAOLIYATIDA KITOBXONLIKNI O'RNI

description: Mazkur maqolada oliy ta’lim muassasalarida talabalarning kitob o‘qishga bo‘lgan rag‘batini oshirish hamda kitob o‘qishning foydalari va bu harakat orqali ta’lim jarayoniga qanday ijobiy ta’sir erish mumkinligi haqida so‘z boradi. Shuningdek, tadqiqot ishiga doir empirik ma’lumotlar keltirilgan. Maqola so‘ngida xulosalar va ishlab chiqilgan ilmiy-metodik tavsiyalar bayon etilgan.
DOI: 10.5281/zenodo.14651973
title: TALABALARNING O'QUV FAOLIYATIDA KITOBXONLIKNI O'RNI

description: Mazkur maqolada oliy ta’lim muassasalarida talabalarning kitob o‘qishga bo‘lgan rag‘batini oshirish hamda kitob o‘qishning foydalari va bu harakat orqali ta’lim jarayoniga qanday ijobiy ta’sir erish mumkinligi haqida so‘z boradi. Shuningdek, tadqiqot ishiga doir empirik ma’lumotlar keltirilgan. Maqola so‘ngida xulosalar va ishlab chiqilgan ilmiy-metodik tavsiyalar bayon etilgan.
DOI: 10.15468/dl.84r2tj
title: Occurrence Download

description: A dataset containing 818 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rodriguezia lanceolata Ruiz & Pav."
 ]
}

 The dataset includes 818 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070687-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kf29wjdmk3.1
title: Dataset with Python Codes for Three-Dimensional Standard Electrocardiogram: A First Approach Based on Precordial Leads

description: This dataset includes the Python codes required to ensure reproducibility of the three-dimensional electrocardiogram generation methodology and its preliminary validation in the context of acute ischemia.
DOI: 10.17632/ss5pfw8xnk.1
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for full year 2023 and 2024 (Jan-Oct). This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012 [4].

This new dataset augments the NSE dataset for 2007-2012 [4], 2013 to 2020 [5], 2021 [6] and 2022 [7]. The data was scrapping from a publicly accessible website [8] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 data columns (1)Date (2)Stock Code (3)Stock Name (4)12-month Low price (5)12-month High price (6)Day's Low price (7)Day's High price (8)Day's Final Price (9)Previous traded price (10)Change in price value (11)Change in price % (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stocks market sector, with 3 columns as: (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2024) of stocks market data including market sector information for bigger opportunities of data analysis and usage in machine learning research.

List of data files on this dataset: 
NSE_data_all_stocks_2023.csv 
NSE_data_all_stocks_2024_jan_to_oct.csv 
NSE_data_stock_market_sectors_2023_2024.csv

References: 
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). 
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. 
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Synergy Systems Ltd. (2023). MyStocks. Retrieved Jan 31, 2023, from http://live.mystocks.co.ke/
DOI: 10.17632/ss5pfw8xnk.2
title: Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024

description: This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for 2023 and 2024. This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012 [4].

This new dataset augments the NSE dataset for 2007-2012 [4], 2013 to 2020 [5], 2021 [6] and 2022 [7]. The data was scrapping from a publicly accessible website [8] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV.

Just like the previous compilations, each stock data row has 13 data columns (1)Date (2)Stock Code (3)Stock Name (4)12-month Low price (5)12-month High price (6)Day's Low price (7)Day's High price (8)Day's Final Price (9)Previous traded price (10)Change in price value (11)Change in price % (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stocks market sector, with 3 columns as: (1)Market sector (2)Stock Code (3)Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2024) of stocks market data including market sector information for bigger opportunities of data analysis and usage in machine learning research.

List of data files on this dataset: 
NSE_data_all_stocks_2023.csv 
NSE_data_all_stocks_2024.csv 
NSE_data_stock_market_sectors_2023_2024.csv

References: 
[1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). 
[2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. 
[3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 
[4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 
[5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 
[6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5
[7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2
[8] Synergy Systems Ltd. (2023). MyStocks. Retrieved Jan 31, 2023, from http://live.mystocks.co.ke/
DOI: 10.15468/dl.bymdfa
title: Occurrence Download

description: A dataset containing 26747 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Asia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pyracantha coccinea M.Roem."
 ]
}

 The dataset includes 26747 records from 415 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070684-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21989/d9/qrwsdg
title: Hospital and Physician Capacity (1996, 2006, 2011, and 2012)

description: &lt;h2&gt;Overview&lt;/h2&gt;
&lt;p&gt;The Dartmouth Institute for Health Policy and Clinical Practice (TDI) has created a publicly available source of data that provides researchers, payers, regulators, and innovators with metrics that quantify temporal and regional patterns of health care spending and utilization in the United States. Using CMS Medicare claims data (mostly for age &gt;64 enrollees), Atlas researchers built cohorts (“denominators”) and numerous measures or events (“numerators”) which were then used to calculate rates either by geography or for patients assigned to specific hospitals. These rates, which are calculated consistently across time and place, provide researchers with opportunities to evaluate spatial and temporal variation/trends. &lt;/p&gt;

&lt;p&gt;In addition to developing rates using Medicare claims data, Atlas researchers developed a variety of measures of hospital and physician capacity by geography using data from the American Hospital Association, the CMS Provider of Services files, CMS Cost Reports, the American Medical Association, and the American Osteopathic Association. This entry contains Dartmouth Atlas rates for these capacity measures, all of which use the number of residents of the geographic area as the denominator. Examples include acute care hospital beds, hospital-based registered nurses, hospital-based employees, physicians, primary care physicians, cardiologists, anesthesiologists, pediatricians, and radiologists. Rates are provided at the hospital referral region (HRR) and hospital service area (HSA) levels, and all rates have been adjusted for age and sex. In contrast to other Atlas rates, these capacity measures were not generated every year; hospital capacity measures are available for 1996, 2006, and 2012, while physician capacity measures are available for 1996, 2006, and 2011. &lt;/p&gt;

&lt;p&gt; Users downloading data should review the methods sections of the related publications for context as well as for information about any temporal changes in methods. All reports in the Dartmouth Atlas of Health Care series are available from the National Library of Medicine &lt;a href="https://www.ncbi.nlm.nih.gov/books/NBK584737/"&gt;https://www.ncbi.nlm.nih.gov/books/NBK584737/&lt;/a&gt;&lt;p&gt;

&lt;p&gt; Note that for the general Dartmouth Atlas rate datasets, which span multiple decades, the author list includes all Dartmouth staff (programmers, analysts, supervisors, etc.) involved in generating all types of Atlas rates across all years. We do not attempt to assign individuals to specific datasets or years. &lt;p&gt;
DOI: 10.17632/9hz6f62gtk.1
title: SNT (Simulated Network Traffic Using Mininet and Ryu) DDoS Detection Dataset

description: Research Hypothesis
The primary hypothesis of this research is that machine learning (ML) models can effectively detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments by leveraging flow-level network traffic features. The dataset was designed to evaluate ML algorithms' ability to distinguish between normal and malicious traffic under realistic network conditions.

What the Data Shows
This dataset captures detailed flow statistics for normal and malicious network traffic:

Normal Traffic: Simulated regular client-server communications in a realistic data center environment.
Malicious Traffic: Traffic generated by launching DDoS attacks such as ICMP flood, UDP flood, TCP SYN flood, and LAND attack.
The dataset provides labeled traffic data, allowing users to train and test ML models for binary classification (normal vs. DDoS) or multi-class classification (by attack type).

Notable Findings
Balanced Data Distribution: The dataset includes 1,034,669 records:
527,576 benign flows (normal traffic).
507,093 malicious flows (DDoS attacks).
Comprehensive Feature Set:
22 flow-level features, such as protocol type, packet counts, byte counts, and traffic duration, enabling detailed analysis of traffic patterns.
High-Quality Labels: Each flow is accurately labeled for supervised ML tasks, with attack traffic optionally classified by type.
How the Data Was Gathered
Simulation Environment:
Mininet: Used to simulate the network topology with multiple hosts and switches.
Ryu SDN Controller: Managed traffic and collected flow statistics via its flow monitoring feature.
Normal Traffic:
Generated using a Python script that simulated client-server interactions with randomized patterns, mimicking real-world user behavior.
Traffic size distribution was modeled on typical data center workloads.
Malicious Traffic:
Created by launching DDoS attacks (e.g., ICMP flood, UDP flood) using tools like hping3.
Attack patterns were randomized to simulate real-world scenarios.
The traffic was monitored, captured, and exported as a labeled CSV file for analysis.

Interpreting the Data
Labels:
1 for normal traffic.
0 for DDoS traffic (or further classified by attack type).
Features: Each row represents a single flow with detailed attributes such as packet counts, duration, and protocol type.
Use Cases:
Train ML models for DDoS detection.
Analyze traffic patterns for feature importance.
Explore SDN-based security and anomaly detection techniques.
Usage Recommendations
This dataset is ideal for researchers and practitioners working on:

DDoS detection and mitigation strategies.
Network traffic analysis in SDN environments.
Feature engineering for network security tasks.
DOI: 10.17632/4ybz4xhcp8.1
title: The relationship between physical fitness and drop vertical jump biomechanics in male college basketball players

description: This database contains physical fitness tests and drop vertical jump biomechanics in male college basketball players.