This dataset is comprised of weekly Spotify track chart data from 2013 to 2023.

The dataset is obtained from https://kworb.net/spotify/ and the official Spotify API. Contrasting to other dataset, this one includes information on the artists, i.e. names and genre. Additionally, the dataset includes the same track multiple times if it appeared in the Spotify charts multiple times, each time with the respective metadata, e.g. chart position and number of streams.

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.

Historical dataset showing Trinidad and Tobago labor force participation rate by year from 1990 to 2024.

About this dataset

Context

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

Content

The dataset constitues the following three datasets

• Victoria, VA Age Group Population Dataset: A complete breakdown of Victoria age demographics from 0 to 85 years, distributed across 18 age groups
• Victoria, VA Age Cohorts Dataset: Children, Working Adults, and Seniors in Victoria - Population and Percentage Analysis
• Victoria, VA 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/.

Exports of Vegetables Nesoi, Fresh Or Chilled in Mexico increased to 346352 USD Thousand in January from 331239 USD Thousand in December of 2023. This dataset includes a chart with historical data for Mexico Exports of Vegetables Nesoi, Fresh Or Chilled.

Time series data for the statistic Paying taxes: Time (hours per year) and country Bosnia and Herzegovina.

These are the SPC BAMEX Forecast Discussion and Charts for June 9 to July 3, 2003. The data includes both gif images of the BAMEX area and HTML files of the forecast discussions.

About this dataset

Context

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

Content

The dataset constitues the following three datasets

• Wilna, New York Age Group Population Dataset: A complete breakdown of Wilna town age demographics from 0 to 85 years, distributed across 18 age groups
• Wilna, New York Age Cohorts Dataset: Children, Working Adults, and Seniors in Wilna town - Population and Percentage Analysis
• Wilna, New York 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/.

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.

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/.

Imports - Composite Diagnostic Or Laboratory Reagents in Mexico increased to 76257 USD Thousand in January from 70988 USD Thousand in December of 2023. This dataset includes a chart with historical data for Mexico Imports of Composite Diagnostic Or Laboratory Rea.

U.S. Transportation and Warehousing Earnings - Historical chart and current data through 2025.

Research-and-Development Time Series for Roku Inc. Roku, Inc., together with its subsidiaries, operates a TV streaming platform in the United States and internationally. The company operates in two segments, Platform and Devices. Its streaming platform allows users to find and access TV shows, movies, news, sports, and others. The company also sells streaming players, Roku-branded TVs, smart home products and services, audio products, and related accessories, as well as offers digital advertising services. Roku, Inc. was incorporated in 2002 and is headquartered in San Jose, California.

Here are the data set and source code related to the paper: "DEVIL: A Framework for Discovering and Evaluating Insidious Advanced Persistent Threats Leveraging Graph-Based Algorithms"

1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections

2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors

3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections

4- countries.zip : contains country name list.

5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/

6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families

7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher

8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip

9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.

10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.

11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:

docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use_neo4j_config=true --env=NEO4J_AUTH=none neo4j:5.13.0

web interface: http://localhost:7474 username: neo4j password: neo4j

Property-Plant-and-Equipment-Gross Time Series for Yidu Tech Inc. Yidu Tech Inc., an investment holding company, provides healthcare solutions built on big data and artificial intelligence (AI) technologies in the People's Republic of China, Brunei, Singapore, and internationally. The company operates through Big Data Platform and Solutions, Life Sciences Solutions, and Health Management Platform and Solutions segments. The Big Data Platform and Solutions segment offers data intelligence platforms and data analytics-driven solutions; AI solutions for a range of medical treatment, education, research, and hospital management scenarios; all disease data platforms; and operates a data center under the Eywa name for hospitals and medical institutions. The Life Sciences Solutions segment offers analytics-driven clinical development, real-world study and evidence, and digital commercialization solutions to pharmaceutical, biotech, medical device, and other companies. The Health Management Platform and Solutions segment provides health management platform application and solution services; operation of YiduCore, an AI medical brain, that extracts, structures, and standardizes the raw and dispersed medical data and transforms it into deep medical insights and practical solutions; distribution of insurance companies' products; and sale of pharmaceutical products and related hardware and other services, as well as offers personalized digital therapies and out-of-hospital care services. It also engages in the sale of medical devices; provision of insurance brokerage and technology services; and medical and computer technology research and development. The company serves healthcare industry participants, including hospitals, research institutions, insurance companies, doctors, patients, and regulators and policy makers, as well as pharmaceutical, biotech, and medical device companies. Yidu Tech Inc. was incorporated in 2014 and is headquartered in Beijing, China.

PPI - Power Cranes and Excavators - Historical chart and current data through 2025.

This polygon shapefile contains ice observations in the Arctic region for December 19, 1988. This layer is part of the Arctic Climate System (ACSYS) Historical Ice Chart Archive. The earliest chart in the data set comes from 1553, when Sir Hugh Willoughby and Richard Chancellor, commanders of two expeditions sent out by the Company of Merchant Adventurers, recorded their observations of the ice edge. Early charts are irregular and infrequent, reflecting the remoteness and hostility of the region. The frequency of observations generally increases over time, as the economic and strategic importance of the Arctic grew, along with the ability to access, observe and record information on sea ice. The Norwegian Meteorological Institute in Tromso used a combination of satellite imagery and in situ observations to produce daily digital charts each working day. These show not only the ice edge, but also detailed information on the range of sea ice concentrations and ice types. The Norwegian Meteorological Institute is continuing this series, and more recent charts may be obtained from this source.

This polygon shapefile contains ice observations in the Arctic region for June 4, 1999. This layer is part of the Arctic Climate System (ACSYS) Historical Ice Chart Archive. The earliest chart in the data set comes from 1553, when Sir Hugh Willoughby and Richard Chancellor, commanders of two expeditions sent out by the Company of Merchant Adventurers, recorded their observations of the ice edge. Early charts are irregular and infrequent, reflecting the remoteness and hostility of the region. The frequency of observations generally increases over time, as the economic and strategic importance of the Arctic grew, along with the ability to access, observe and record information on sea ice. The Norwegian Meteorological Institute in Tromso used a combination of satellite imagery and in situ observations to produce daily digital charts each working day. These show not only the ice edge, but also detailed information on the range of sea ice concentrations and ice types. The Norwegian Meteorological Institute is continuing this series, and more recent charts may be obtained from this source.

This polygon shapefile contains ice observations in the Arctic region for May 2, 2000. This layer is part of the Arctic Climate System (ACSYS) Historical Ice Chart Archive. The earliest chart in the data set comes from 1553, when Sir Hugh Willoughby and Richard Chancellor, commanders of two expeditions sent out by the Company of Merchant Adventurers, recorded their observations of the ice edge. Early charts are irregular and infrequent, reflecting the remoteness and hostility of the region. The frequency of observations generally increases over time, as the economic and strategic importance of the Arctic grew, along with the ability to access, observe and record information on sea ice. The Norwegian Meteorological Institute in Tromso used a combination of satellite imagery and in situ observations to produce daily digital charts each working day. These show not only the ice edge, but also detailed information on the range of sea ice concentrations and ice types. The Norwegian Meteorological Institute is continuing this series, and more recent charts may be obtained from this source.

Switzerland: Population ages 65 and above, percent of total: The latest value from 2024 is 20.02 percent, an increase from 19.61 percent in 2023. In comparison, the world average is 10.43 percent, based on data from 196 countries. Historically, the average for Switzerland from 1960 to 2024 is 14.65 percent. The minimum value, 10.23 percent, was reached in 1960 while the maximum of 20.02 percent was recorded in 2024.