The Federal Aviation Administration (FAA) digital-Visual Chart series is designed to meet the needs of users who require georeferenced raster images of FAA Visual Flight Rules (VFR) Terminal Area charts. An Aeronautical Raster Chart is a digital image of an FAA VFR Chart. All information that is part of the paper chart is included in the file. The area inside the neat line is georeferenced to the surface of the earth. Only the main body of the chart is accurately georeferenced. Each digital-Visual Chart is provided in one TIF file. The files are 300 dots per inch and 8-bit color.

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.

Patient-drug-disease (PDD) Graph dataset, utilising Electronic medical records (EMRS) and biomedical Knowledge graphs. The novel framework to construct the PDD graph is described in the associated publication.PDD is an RDF graph consisting of PDD facts, where a PDD fact is represented by an RDF triple to indicate that a patient takes a drug or a patient is diagnosed with a disease. For instance, (pdd:274671, pdd:diagnosed, sepsis)Data files are in .nt N-Triple format, a line-based syntax for an RDF graph. These can be accessed via openly-available text edit software.diagnose_icd_information.nt - contains RDF triples mapping patients to diagnoses. For example:(pdd:18740, pdd:diagnosed, icd99592),where pdd:18740 is a patient entity, and icd99592 is the ICD-9 code of sepsis.drug_patients.nt- contains RDF triples mapping patients to drugs. For example:(pdd:18740, pdd:prescribed, aspirin),where pdd:18740 is a patient entity, and aspirin is the drug's name.Background:Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Faced with patients' symptoms, experienced caregivers make the right medical decisions based on their professional knowledge, which accurately grasps relationships between symptoms, diagnoses and corresponding treatments. In the associated paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint as well as in .nt format in this repository, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.De-identificationIt is necessary to mention that MIMIC-III contains clinical information of patients. Although the protected health information was de-identifed, researchers who seek to use more clinical data should complete an on-line training course and then apply for the permission to download the complete MIMIC-III dataset: https://mimic.physionet.org/

GDP deflator: linked series (base year varies by country) in Norway was reported at 137 year in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Norway - GDP deflator: linked series (base year varies by country) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The ARG Database is a huge collection of labeled and unlabeled graphs realized by the MIVIA Group. The aim of this collection is to provide the graph research community with a standard test ground for the benchmarking of graph matching algorithms.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis (DGS30) from 1977-02-15 to 2025-07-10 about 30-year, maturity, Treasury, interest rate, interest, rate, and USA.

About this dataset

Context

The dataset tabulates the University Heights population by age. The dataset can be utilized to understand the age distribution and demographics of University Heights.

Content

The dataset constitues the following three datasets

• University Heights, OH Age Group Population Dataset: A complete breakdown of University Heights age demographics from 0 to 85 years, distributed across 18 age groups
• University Heights, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in University Heights - Population and Percentage Analysis
• University Heights, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Inflation, GDP deflator: linked series (annual %) in Guatemala was reported at 6.6423 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Guatemala - Inflation, GDP deflator: linked series (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Graph and download economic data for Total Construction Spending: Residential in the United States (TLRESCONS) from Jan 2002 to Apr 2025 about residential, expenditures, construction, and USA.

Current Effective Date: 0901Z 12 Jun 2025 to 0901Z 07 Aug 2025A Holding Pattern is a predetermined maneuver which keeps an aircraft within a specified airspace while awaiting further clearance. This Holding Pattern data is provided as a geospatial vector file format and depicted on Enroute charts. Holding Pattern information is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.

GDP: linked series (current LCU) in Egypt was reported at 10157360200000 LCU in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Egypt - GDP at market prices: linked series (current LCU) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

About this dataset

Context

The dataset tabulates the Valley Springs population by age. The dataset can be utilized to understand the age distribution and demographics of Valley Springs.

Content

The dataset constitues the following three datasets

• Valley Springs, SD Age Group Population Dataset: A complete breakdown of Valley Springs age demographics from 0 to 85 years, distributed across 18 age groups
• Valley Springs, SD Age Cohorts Dataset: Children, Working Adults, and Seniors in Valley Springs - Population and Percentage Analysis
• Valley Springs, SD Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)—each essential parameters in wavelet-based methods—on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.

Salmonella pangenome graph and variant call data for 539,283 genomesDescription:Salmonella enterica causes human disease and decreases agricultural production. The overall goals of this project is to generate a large database of S. enterica variants with 539,283 samples and 236,069 features for applications in machine learning and genomics. We transformed single nucleotide polymorphism (SNP) data into reduced dimensional representations which are tolerant of missing data based on disentangled variational autoencoders. TFRecord files were made with custom Python scripts that parsed the variant call formats (VCF) into sparse tensors and combined them with the Salmonella In Silico Typing Resource (SISTR) serotype data.The data directory contains:The tar file of TFRecords: tfrecords.tar (103 GB). The TFRecords are organized first by how they were genotyped. mpileup records were created with Mpileup, and the gvg records were created with graph variant calling. In each of these directories batches of ~10,000 sequence reads named Sra10k_XX.tfrecord.gz (00--54). File Sra10k_99.tfrecord.gz contains incomplete SRAs. Each TFRecord contains the shape of the tensor, the indices of non-zero variants, sample name, serotype, and sparse values. Value 99 was assigned to '.' records.The file output.tar (11.4 TB) contains the .vcf files used to create the TFRecords above. The data in here is contained more succinctly in the TTFrecord format. This data will not normally be used.A tar file of metadata files for the samples, metadata (95 MB). Sequence read archive (SRA) accessions were downloaded using edirect/eutilities and saved as SraAccList.txt.esearch -db sra -query "txid28901[Organism:exp] AND (cluster_public[prop] AND 'biomol dna'[Properties] AND 'library layout paired'[Properties] AND 'platform illumina'[Properties] AND 'strategy wgs'[Properties] OR 'strategy wga'[Properties] OR 'strategy wcs'[Properties] OR 'strategy clone'[Properties] OR 'strategy finishing'[Properties] OR 'strategy validation'[Properties])" | efetch -format runinfo -mode xml | xtract -pattern Row -element Run > SraAccList.txtGoogle BigQuery was used to download metadata for the SRA accessions from the National Institute of Health (NIH).SELECT * FROM nih-sra-datastore.sra.metadata as metadata INNER JOIN {table_id} as leiacc ON metadata.acc = leiacc.accID;Files were processed into batches of ~10,000 and named Sra_completed_XX.csv (00--53).A VCF document mapping the TFRecord data to the positions in the graph subjected to the Type strain LT2: mapping/DRR452337.gvg.vcf-with_TFRecord_in_1st_column.txtScripts for creating and reading TFRecord data: code.reading_and_parsing_fns.py defines functions for converting VCFs of variants called using gvg to sparse tensors and makes the TFRecord files.gvg_to_tfrecord.py creates TFRecords from from the sparse tensors.Tutorial for using the TFRecords: Example_logistic_regression.mdPangenome graph files and references used for variant calling and genotyping: pangenome.refPlus100.fasta.gz which contains the genomes of the 101 Salmonella strains without plasmids used for construction of the pangenome graph.salm.100.NC_003197_v2.d2_complete.gfa.gz The complete 101 Salmonella strain pangenome graph in Graphical Fragment Assembly (GFA2) Format 2.0 including alt nodes used for genotypingsalm.100.NC_003197_v2.full.gfa.gz the full graph including alt nodes.salm.100.NC_003197_v2.full.vcf.gz A VCF of the file abovegenotyped.gvg.vcf the genotype calls in vcf formatpaths.txt the paths of the graphSCINet users: The data folder can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node28083194/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to access this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

Graph and download economic data for CBOE NASDAQ 100 Volatility Index (VXNCLS) from 2001-02-02 to 2025-07-10 about VIX, volatility, stock market, and USA.

About this dataset

Context

The dataset tabulates the West Farmington population by age. The dataset can be utilized to understand the age distribution and demographics of West Farmington.

Content

The dataset constitues the following three datasets

• West Farmington, OH Age Group Population Dataset: A complete breakdown of West Farmington age demographics from 0 to 85 years, distributed across 18 age groups
• West Farmington, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in West Farmington - Population and Percentage Analysis
• West Farmington, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the Westfield population by age. The dataset can be utilized to understand the age distribution and demographics of Westfield.

Content

The dataset constitues the following three datasets

• Westfield, IL Age Group Population Dataset: A complete breakdown of Westfield age demographics from 0 to 85 years, distributed across 18 age groups
• Westfield, IL Age Cohorts Dataset: Children, Working Adults, and Seniors in Westfield - Population and Percentage Analysis
• Westfield, IL Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

GDP: linked series (current LCU) in San Marino was reported at 1739426829 LCU in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. San Marino - GDP at market prices: linked series (current LCU) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Palestine: Tertiary school enrollment, percent of all eligible children: The latest value from 2023 is 42.91 percent, a decline from 44.98 percent in 2022. In comparison, the world average is 40.35 percent, based on data from 63 countries. Historically, the average for Palestine from 1996 to 2023 is 38.36 percent. The minimum value, 15.86 percent, was reached in 1996 while the maximum of 48.41 percent was recorded in 2011.

Real Gross Domestic Product (Euro/ECU series) for Slovakia was 21159.10000 Mil. of Chn. 2010 Euros in July of 2020, according to the United States Federal Reserve. Historically, Real Gross Domestic Product (Euro/ECU series) for Slovakia reached a record high of 21774.00000 in October of 2019 and a record low of 8663.10000 in January of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for Real Gross Domestic Product (Euro/ECU series) for Slovakia - last updated from the United States Federal Reserve on June of 2025.