Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Digital Index of North American Archaeology (DINAA)" data publication.

Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 45.958 NA in 2022. This records a decrease from the previous number of 49.075 NA for 2021. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 49.892 NA from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 52.417 NA in 2018 and a record low of 45.958 NA in 2022. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Algeria – Table DZ.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

The data set presents the results of the quality assessment of data sources by the working group on data collection for the identification of emerging risks related to food and feed. For this assessment, the WG defined text descriptors and quality parameters (i.e. link with indicators, data type, geographic and period coverage, language, edition, timeliness, accessibility, clarity and comparability). These data sources were linked to eleven priority indicators (i.e. the ESCO indicators) and qualitatively assessed and profiled.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅232M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

Japan JP: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 84.050 NA in 2024. This stayed constant from the previous number of 84.050 NA for 2023. Japan JP: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 78.317 NA from Mar 2017 (Median) to 2024, with 8 observations. The data reached an all-time high of 84.050 NA in 2024 and a record low of 71.542 NA in 2017. Japan JP: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Data sources’ characteristics*.

Differentiation of data sources such as terrestrial mapping in protected areas/areas worthy of protection, biotope mapping otherwise. Natural and open spaces through aerial photo analysis and secondary data on settlement biotopes, green spaces, allotments, cemeteries.

Maximum Analysis Sample Sizes by Analysis Type and Data Source.

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

Secondary data and baseline covariates of patients included in DISCOVER CKD.

Palau SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 53.817 NA in 2023. This stayed constant from the previous number of 53.817 NA for 2022. Palau SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 53.317 NA from Sep 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 53.817 NA in 2023 and a record low of 52.817 NA in 2021. Palau SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Palau – Table PW.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Table 1. Comparison with other integrated RNA modification related databasesTable S1. MeRIP-Seq datasets information in PRMD.Table S2. Data sources and bioinformatics workflow used tools in PRMD.Table S3. The data sources from previous published research articles.Table S4. Other types of RNA modifications.Table S5. The predicted m6A sites for 20 species.Table S6. The results of RMplantVar analysis.

This dataset presents a compendium of field-based earthworm data sources and associated meta-data from across the United Kingdom and Ireland (‘Worm source’). These were compiled up to 2021 and include 257 data sources, the earliest dating back to 1891. Source meta-data covers the type of quantitative earthworm data (i.e. incidence, abundance, biomass, taxa), methodological details (e.g. sampling method/s, location/s, whether sampled plots were natural or experimental, sampling year/s), and environmental information (e.g. habitat/land-use, inclusion of climate data and basic soil properties). Data sources were collected through literature searches on Web of Science and Google Scholar, as well as directly from original authors/data holders where possible. The data sources were compiled with the aim of gathering quantitative data on earthworm species and populations to develop earthworm abundance and niche models, and toward a modelling framework for earthworm impacts on soil processes. This work is part of the Soil Organic Carbon Dynamics (SOC-D) project funded by the NERC UK-SCAPE programme (Grant reference NE/R016429/1). Full details about this dataset can be found at https://doi.org/10.5285/1a1000a8-4e7e-4851-8784-94c7ba3e164f

As the primary goal of the 17 Sustainable Development Goals (SDGs), poverty eradication is still one of the major challenges faced by countries around the world, and relative poverty is a comprehensive poverty pattern triggered by the superposition of economic, social, and environmental dimensions. Therefore, Therefore, this paper introduces the perspective of coupled coordination to consider the formation of relative poverty, constructs indicators in three major dimensions: economic, social, and environmental, proposes a fast and more accurate method of identifying relative poverty in a region by using machine learning, measures the degree of coupled coordination of China’s relatively poor provinces using a coupled coordination model and analyzes the relationship with the level of relative poverty, and puts forward suggestions for poverty management on this basis using typology classification. The results of the study show that: 1) the fusion of data crawlers, remote sensing space, and other multi-source data to construct the dataset and propose a fast and efficient regional relative poverty identification method based on big data with low comprehensive cost and high identification accuracy of 0.914. 2) Currently, 70.83% of the economic-social-environmental systems of the relatively poor regions are in the dysfunctional type and are in a state of disordered development and malignant constraints. The regions showing coupling disorders are mainly clustered in the three southern prefectures of Xinjiang, Qinghai, Gansu, Yunnan, and Sichuan, and their spatial distribution is relatively concentrated. 3) The types of poverty and their coupled and coordinated development in each region show large spatial variability, requiring differentiated poverty eradication countermeasures tailored to local conditions to achieve sustainable regional economic-social-environmental development.

North Macedonia MK: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 55.983 NA in 2019. This stayed constant from the previous number of 55.983 NA for 2018. North Macedonia MK: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 57.479 NA from Dec 2016 (Median) to 2019, with 4 observations. The data reached an all-time high of 59.250 NA in 2016 and a record low of 55.983 NA in 2019. North Macedonia MK: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s North Macedonia – Table MK.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

IncRML resources

This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our experiment tool written in Python which was already used before in the Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.

Datasets

We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.

Benchmarks

GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)

GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)

GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)

Real-world datasets

Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)

Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)

Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)

Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)

Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)

Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)

OpenStreetMap (World): geographical map data (1 day, 1440 versions)

Ingestion

Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.

Remarks

The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.

GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz

All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).

Reproducing

By using our experiment tool, you can easily reproduce the experiments as followed:

Download one of the TAR.XZ archives and unpack them.

Clone the GitHub repository of our experiment tool and install the Python dependencies with 'pip install -r requirements.txt'.

Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.

Execute the tool by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run'. The argument '--runs=5' is used to perform the experiment 5 times.

Once executed, you can generate the statistics by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive stats'.

Testcases

Testcases to verify the integration of RML and LDES with IncRML, see https://doi.org/10.5281/zenodo.10171394

Gabon GA: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 33.225 NA in 2023. This stayed constant from the previous number of 33.225 NA for 2022. Gabon GA: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 15.108 NA from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 33.225 NA in 2023 and a record low of 13.283 NA in 2018. Gabon GA: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gabon – Table GA.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;