Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL), is being developed by the Main Bioinformatics Laboratory at Washington State University in collaboration with the USDA-ARS Grain Legume Genetics and Physiology Research Unit, the USDA-ARS Plant Germplasm Introduction and Testing Unit, the USA Dry Pea and Lentil Council, Northern Pulse Growers and allied scientists in the US and across the world, to serve as a resource for Genomics-Assisted Breeding (GAB). GAB offers tools to identify genes related to traits of interest among other methods to optimize plant breeding efficiency and research, by providing relevant genomic, genetic and breeding information and analysis. Therefore, tools such as JBrowse and MapViewer can be found in this database, as well as key resources to provide the access to the annotation of available transcriptome data, helping pulse breeders and researchers to succeed in their programs. Resources in this dataset:Resource Title: Pulse Crop Database Resources. File Name: Web Page, url: https://www.pulsedb.org/ Resources include data submission and download, and search by gene and transcript, germplasm, map, marker, publication, QTL, sequence, megasearch, and trait/descriptor. A User Manual describes how to access data and use the tools on the Pulse Crop Database. Tools supported: BLAST, JBrowse, PathwayCyc, MapViewer, and Synteny Viewer

The database includes 477 subjects (54% male, 46% female). Data were collected from three hospitals: the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Liuyang Hospital of Traditional Chinese Medicine, and the Affiliated Hospital of Hunan University of Traditional Chinese Medicine, between 2016 and 2017, from individuals undergoing physical examinations. The data primarily consisted of pulse waveform signals, age, gender, systolic blood pressure, diastolic blood pressure, and BMI. This study utilized a pulse data acquisition device developed by the Chinese Academy of Medical Sciences (CACMS) (Patent No. 200810225717.0), which employs pressure sensors. The device is capable of simultaneously collecting pulse data from three channels on a single wrist at a sampling frequency of 1000 Hz. Additionally, the device automatically saves the data collected by the sensors to the local disk. Prior to testing, all participants rested for at least 3 minutes. Pulse data from six channels were then sequentially recorded, with each recording lasting approximately 30 seconds. During the test, participants were instructed to sit upright with their arms relaxed, ensuring that the core and position remained nearly level throughout the data collection process. The device also allows for adjustment of the contact pressure on the skin to obtain clear pulse data. For the purposes of this database, only the pulse data from a single channel was selected.

The Pulse Wave Database

The Pulse Wave Database (PWDB) is a database of simulated arterial pulse waves designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. The entire database is available at DOI: 10.5281/zenodo.2633174 .

This dataset: baseline subjects aged 25 to 75

This dataset is a subset of the PWDB. It contains the pulse waves for the six baseline subjects aged 25 to 75 (in 10 year increments). It contains the following waves:

arterial flow velocity (U),

luminal area (A),

pressure (P), and

photoplethysmogram (PPG).

These pulse waves are provided at a range of measurement sites, including:

aorta (ascending and descending)

carotid artery

brachial artery

radial artery

finger

femoral artery

The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database

Accompanying Publication

This is a subset of the PWDB database, which is described in the following publication:

Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices,” [under review]

Please cite this publication when using the database.

Further Information

Further information on the Pulse Wave Database project can be found at: https://peterhcharlton.github.io/pwdb/

Version History

Version 1.0 : provided for peer review of "Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices"

This provides a link to the version of the Pulse Wave Database which was made available through PhysioNet for the purposes of peer review.This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It will contain pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database will contain a baseline set of pulse waves for each of the six age groups, which was created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It will also contain 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive in silico analyses of the performance of pulse wave analysis algorithms.

Overview

This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive in silico analyses of haemodynamics and the performance of pulse wave analysis algorithms.

Data Description

The database contains the following waves:

arterial flow velocity (U),

luminal area (A),

pressure (P), and

photoplethysmogram (PPG).

These pulse waves are provided at a range of measurement sites, including:

aorta (ascending and descending)

carotid artery

brachial artery

radial artery

finger

femoral artery

The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database

Accompanying Publication

The database is described in the following publication:

Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices,” [under review]

Please cite this publication when using the database.

Further Information

Further information on the Pulse Wave Database project can be found at: https://peterhcharlton.github.io/pwdb/

This provides a brief overview of the database. Further details are provided at: https://peterhcharlton.github.io/pwdb/ppwdb.html

Background: The shape of the arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health, since it is influenced by both the heart and the vasculature. Consequently, many algorithms have been proposed to estimate clinical parameters from PWs. However, it is difficult and costly to acquire comprehensive datasets with which to assess their performance. We are aiming to address this difficulty by creating a database of simulated PWs under a range of CV conditions, representative of a healthy population. The database provided here is an initial version which has already been used to gain some novel insights into haemodynamics.

Methods: Baseline PWs were simulated using 1D computational modelling. CV model parameters were varied across normal healthy ranges to simulate a sample of subjects for each age decade from 25 to 75 years. The model was extended to simulate photoplethysmographic (PPG) PWs at common measurement sites, in addition to the pressure (ABP), flow rate (Q), flow velocity (U) and diameter (D) PWs produced by the model.

Validation: The database was verified by comparing simulated PWs with in vivo PWs. Good agreement was observed, with age-related changes in blood pressure and wave morphology well reproduced.

Conclusion: This database is a valuable resource for development and pre-clinical assessment of PW analysis algorithms. It is particularly useful because it contains several types of PWs at multiple measurement sites, and the exact CV conditions which generated each PW are known.

Future work: However, there are two limitations: (i) the database does not exhibit the wide variation in cardiovascular properties observed across a population sample; and (ii) the methods used to model changes with age have been improved since creating this initial version. Therefore, we are currently creating a more comprehensive database which addresses these limitations.

Accompanying Presentation: This database was originally presented at the BioMedEng18 Conference. The presentation describing the methods for creating the database, and providing an introduction to the database, is available at: https://www.youtube.com/watch?v=X8aPZFs8c08 . The accompanying abstract is available here.

Accompanying Manual: Further information on how to use the PWDB datasets, including this preliminary dataset, are provided in the user manual. Further details on the contents of the dataset files are available here.

Citation: When using this dataset please cite this publication:

Charlton P.H. et al. Modelling arterial pulse wave propagation during healthy ageing, In World Congress of Biomechanics 2018, Dublin, Ireland, 2018.

Version History:

• v.1.0: Originally uploaded to PhysioNet. This is the version which was used in the accompanying presentation.

• v.2.0: The initial upload to this DOI. The database was curated using the PWDB Algorithms v.0.1.1. It differs slightly from the originally reported version in that: (i) the augmentation pressure and index were calculated at the aortic root rather than the carotid artery.

Text adapted from: Charlton P.H. et al., 'A database for the development of pulse wave analysis algorithms', BioMedEng18, London, 2018.

🇺🇸 미국

The sun pulse data set is a record of times that the Lunar Prospector sun sensor detects the Sun. These sun pulse times are derived from ground processing of the sun sensor data, earth-moon limb sensor data, and the spacecraft ephemeris. This data set is part of the Lunar Prospector Level 0 data archive. The sun pulse times are used in determining the spacecraft spin rate and orientation as a function of time. Because the Lunar Prospector spacecraft is sometimes in eclipse with the Sun, the sun pulse data set is generated on the ground to include both actual sun pulse times based on the sun sensor measurements and estimated sun pulse times based on the history of spin rate change during all prior eclipses.

GPI pulse™ is a dynamic value, price, and access analytics platform, utilising powerful algorithms to combine multiple data streams in a way that will give you unique insight into the full market access journey.

This powerful platform is designed to be used as part of GPI’s comprehensive solution for pricing and market access strategy, but can also be leveraged to rapidly extract customised sets of analysis-ready data.

GPI pulse™ offers pharmaceutical pricing and reimbursement data across 95+ markets, covering all therapeutic areas. The provision of this data requires real-time, dynamic collation from a multitude of global sources with individual formats and update schedules.

• Prices: List price data is stored at pack level. To track all prices from ex-factory to reimbursed prices, GPI also applies a series of calculations to provide a clear view across the supply chain.

• Reimbursement: Payer compensation and restrictions applied at pack and product level.

Use Cases: - Quickly view global availability of key competitors or analogues - Access list prices and track prices across the supply chain - Understand how therapies have been restricted in certain markets - Track list price changes over time

GPI continually invest in technology, taxonomy, and quality assurance, to ensure our data is the gold-standard in terms of accuracy, granularity, and timeliness. GPI pulse™ supports the full spectrum of biopharma clients, from top tier pharma to clinical stage biotech. The analytics tools within GPI pulse™ are built specifically with pricing, market access, and commercial strategy teams in mind.

FREE DATA SAMPLES AVAILABLE: - Pricing - Cost of Treatment - Reimbursement and HTA

This is an updated Parkes transient database (PTD II) for https://data.csiro.au/collection/csiro:42640. PTD II contains 165,592 single pulses from 363 known pulsars. Unlike previous databases while maintaining a compact size of only 1.5 GB. The database employs a sqlite3 structure organising pulsar metadata, observation files, and pulse events with their raw data stored in binary format. We provide processing tools (get_pulsar_pub.py) for extracting and analysing single-pulse data, enabling fluence fitting and statistical analysis.

The peer-reviewed paper associated with this dataset has now been published in Scientific Data, and can be accessed here: https://www.nature.com/articles/sdata201820. Please cite this when using the dataset.

Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data records from 219 subjects. The dataset covers an age range of 20–89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.

PhonePe Pulse is a first-of-its-kind product in India that showcases data about more than ₹. 2000+ crore transactions by consumers. It provides data-driven insights and trends from the Indian digital payments industry based on PhonePe transaction data across India. This dataset contains data from PhonePe Pulse, covering the years 2018-2022 across all quarters. It provides insights into digital payment trends and patterns in India during this time period.

Dataset

This dataset was created by Aaron1 Jiang

Contents

19 Global import shipment records of Pulse Generator with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This data release includes geodetic time series from high-rate GPS instruments recording 4 earthquakes co-seismically in the near-field – the 2010 Maule, Chile earthquake; the 2012 Nicoya, Costa Rica earthquake; the 2014 Iquique, Chile earthquake; and the 2015 Gorkha, Nepal earthquake. For each earthquake, data (sac files, 1 Hz sampling, ~2-3 minutes around the earthquake origin time) are included in a separate folder. Each sac file provides a time series of ground displacement from the earthquake as recorded at that station. The location of each station is listed in the relevant earthquake file in the “_station_info” folder.

For the Minute 319 pulse flow, electromagnetic induction surveys were carried out jointly by the USGS and UABC-CICESE. Measurements were made at 5 m intervals and transect lengths ranged from 55 m to 200 m.

899 Global import shipment records of Pulse Oximeter with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Motivation

This dataset is derived and cleaned from the full PULSE project dataset to share with others data gathered about the users during the project.

Disclaimer

Any third party need to respect ethics rules and GDPR and must mention “PULSE DATA H2020 - 727816” in any dissemination activities related to data being exploited. Also, you should provide a link to the project associated website: http://www.project-pulse.eu/

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Description of the dataset

The only difference with the original dataset comes from anonymised user information.

The dataset content is described in a dedicated JSON file:

{ "citizen_id": "pseudonymized unique key of each citizen user in the PULSE system", "city_code": { "description": "3-letter city codes taken by convention from IATA codebook of airports and metropolitan areas, as the codebook of global cities in most common and widespread use and therefore adopted as standard in PULSE (since there is currently - in the year 2020 - still no relevant ISO or other standardized codebook of cities uniformly globally adopted and used). Exception is Pavia which does not have its own airport,and nearby Milan/Bergamo airports are not applicable, so the 'PAI' internal code (not existing in original IATA codes) has been devised in PULSE. For cities with multiple airports, IATA metropolitan area codes are used (New York, Paris).", "BCN": "Barcelona", "BHX": "Birmingham", "NYC": "New York", "PAI": "Pavia", "PAR": "Paris", "SIN": "Singapore", "TPE": "Keelung(Taipei)" }, "zip_code": "Zip or postal code (area) within a city, basic default granular territorial/administrative subdivision unit for localization of citizen users by place of residence (in all PULSE cities)", "models": { "asthma_risk_score": "PULSE asthma risk consensus model score, decimal value ranging from 0 to 1", "asthma_risk_score_category": { "description": "Categorized value of the PULSE asthma risk consensus model score, with the following possible category options:", "low": "low asthma risk, score value below 0,05", "medium-low": "medium-low asthma risk, score value from 0,05 and below 0,1", "medium": "medium asthma risk, score value from 0,1 and below 0,15", "medium-high": "medium-high asthma risk, score value from 0,15 and below 0,2", "high": "high asthma risk, score value from 0,2 and higher" }, "T2D_risk_score": "PULSE diabetes type 2 (T2D) risk consensus model score, decimal value ranging from 0 to 1", "T2D_risk_score_category": { "description": "Categorized value of the PULSE diabetes type 2 risk consensus model score, with the following possible category options:", "low": "low T2D risk, score value below 0,05", "medium-low": "medium-low T2D risk, score value from 0,05 and below 0,1", "medium": "medium T2D risk, score value from 0,1 and below 0,15", "medium-high": "medium-high T2D risk, score value from 0,15 and below 0,2", "high": "high T2D risk, score value from 0,2 and below 0,25", "very_high": "very high T2D risk, score value from 0,25 and higher" }, "well-being_score": "PULSE well-being model score, decimal value ranging from -5 to 5", "well-being_score_category": { "description": "Categorized value of the PULSE well-being model score, with the following possible category options:", "low": "low well-being, score value below -0,37", "medium-low": "medium-low well-being, score value from -0,37 and below 0,04", "medium-high": "medium-high well-being, score value from 0,04 and below 0,36", "high": "high well-being, score value from 0,36 and higher" }, "computed_time": "Timestamp (UTC) when each relevant model score value/result had been computed or derived"
} }

The purpose of the present study was to examine how information source (control—no source, USDA, fictitious hospital, or fictitious social media) impacts perceptions of diet information. Participants included 943 American adults who were aged 18-74 years (M = 37.51, SD = 9.50) and were recruited from across the United States through Amazon Mechanical Turk (MTurk). As a manipulation check we assessed whether participants accurately completed the manipulation by ensuring their response to the question of who made the flyer. Participants who answered the question incorrectly were excluded from the analysis. In total, 537 answered correctly and were included in the analyses (Control = 113, Hospital = 144, Social Media = 121, USDA = 159). The majority of our eligible sample identified as men (N = 350), while the remainder identified as women (N = 185), nonbinary (N = 1), or “other” (N = 1).Participants completed an online survey in which they viewed one flyer containing dietary information and guidance on consuming pulses. The purported source of the flyer information was manipulated to create the 4 conditions. Participants rated the flyer in terms of perceived accuracy, trustworthiness, reliability, desirability for learning more from the source, and likelihood of following the advice. Attitudes, perceived control and norms, and past behavior were used to measure components of the Theory of Planned Behavior (TPB). ANOVA results indicated that the USDA and hospital sources were perceived as more accurate, trustworthy, reliable, and more desirable to learn more from relative to control and social media. There were no differences in likelihood of following guidance depending on source. Multiple regression showed that measures of the TPB were predictors of likelihood of following advice. Participants also ranked their top 3 most trusted sources for health information from a list of 29 sources. Doctors, scientists, nurses, and family and friends were among the most frequently trusted sources. Overall, these findings suggest that trust in the source of information does not influence perceived likelihood of following dietary recommendations for pulses. Resources in this dataset:Resource Title: Effect of Source on Trust of Pulse Nutrition Information and Perceived Likelihood of Following Dietary Guidance. File Name: EffectofSource_Data.xlsxResource Description: One-way analyses of variance (ANOVA) were used to assess between-condition differences for ratings of each of the 5 primary dependent variables (i.e., perceptions of the flyer; variables named Flyer_InfoAccuracy, Flyer_TrustInSource, Flyer_SourceReliability, Flyer_LearnMore, Flyer_FollowAdvice). Tukey tests were used to examine all pairwise comparisons for each of the significant ANOVA effects. A bivariate Pearson correlation was used to examine the relationship between trust in source and likelihood of following advice (variables Flyer_TrustInSource and Flyer_FollowAdvice). Multiple regression/correlation (MRC) was used to assess whether components of the TPB (TPB_Attitudes1, TPB_Attitudes2, TPB_PerceivedNorms1, TPB_PerceivedNorms2, TPB_PerceivedControl1, TPB_PerceivedControl2, TPB_PastBehavior) were predictive of likelihood of following advice (Flyer_FollowAdvice). Finally, frequency data was used to assess percentage with which participants selected sources as being in their top 3 most trusted (Trust_Ald_2_0_GROUP1-Trust_Ald_2_0_29_RANK). Sources that were selected are noted as either 1, 2, or 3 depending on rank, and the sources participants did not select are listed as #NULL!. Data was analyzed using SPSS statistical software, version 28. Resource Software Recommended: SPSS,url: https://www.ibm.com/products/spss-statistics?utm_content=SRCWW&p1=Search&p4=43700050715561164&p5=e&gclid=EAIaIQobChMI2fnV4I6e-AIVErfICh00pwcfEAAYASAAEgIkHfD_BwE&gclsrc=aw.ds

Find details of Pulse Sports Llc Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.