Study 1 Text preparation (specific questionnaire questions can be found in the paper) Vocabulary 1: 845 words related to material wealth and spiritual wealth, as well as their relationship, in The Contemporary Chinese Dictionary (7th edition); Vocabulary 2: Further screening, deleting irrelevant words, merging synonyms, and organizing a total of 69 sets of vocabulary. Test (detailed information can be found in the paper, variable labels and meanings can be found in SPSS data) Data 1: In August 2021, questionnaires were distributed through online platforms with an IP address limited to Zhejiang Province. A total of 503 responses were received, and invalid responses such as short answer times and regular responses were deleted, resulting in 462 valid responses (91.85%). Data 2: In September 2021, questionnaires were distributed through online platforms with IP addresses limited to Zhejiang Province. A total of 208 responses were received, and invalid responses such as short response times and regular responses were deleted, resulting in 201 valid responses (96.63%). Study 2 Test (detailed information can be found in the paper, variable labels and meanings can be found in SPSS data) Data 3: From July to August 2023, questionnaires were distributed through online platforms with IP addresses limited to Zhejiang Province. A total of 1045 answer sheets were collected. Deleting invalid answers such as short answer times and regular responses resulted in 937valid responses (89.67%).

The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc.

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Data Governance Software Market size was valued at USD 4.18 Billion in 2024 and is projected to reach USD 20.97 Billion by 2031, growing at a CAGR of 22.35% from 2024 to 2031.

Global Data Governance Software Market Drivers

Data Privacy Regulations: The increasing stringency of data privacy regulations such as GDPR, CCPA, and HIPAA mandates organizations to implement robust data governance practices. Data governance software helps companies ensure compliance with these regulations by managing data access, usage, and security.

Data Security Concerns: With the growing frequency and sophistication of cyber threats, organizations prioritize data security. Data governance software provides tools for defining and enforcing data security policies, monitoring data access and usage, and detecting and mitigating security breaches.

Data Quality Improvement: Poor data quality can lead to errors, inefficiencies, and inaccurate decision-making. Data governance software helps organizations establish data quality standards, define data quality metrics, and implement processes for data cleansing, validation, and enrichment to improve overall data quality.

Increasing Data Volumes and Complexity: Organizations are dealing with ever-increasing volumes of data from various sources, including structured and unstructured data, IoT devices, social media, and cloud applications. Data governance software helps manage this complexity by providing tools for data discovery, classification, and lineage tracking.

Digital Transformation Initiatives: Organizations undergoing digital transformation initiatives recognize the importance of data governance in ensuring the success of these initiatives. Data governance software facilitates data integration, collaboration, and governance across disparate systems and data sources, supporting digital transformation efforts.

Risk Management and Compliance: Effective data governance is essential for managing risks associated with data breaches, regulatory non-compliance, and reputational damage. Data governance software enables organizations to identify, assess, and mitigate risks related to data management and usage.

Metadata Management Solutions Market Outlook

The global metadata management solutions market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 12 billion by 2032, exhibiting a robust CAGR of 14.5% during the forecast period. This substantial growth is primarily driven by the increasing adoption of data governance practices and the rising need for data quality and compliance across various industries.

The growing importance of data as a strategic asset is fueling the demand for metadata management solutions. Organizations are increasingly recognizing the value of metadata in improving data visibility, accessibility, and governance. By providing a unified view of data across disparate systems, metadata management solutions enable businesses to harness the power of data analytics, thereby driving better decision-making and operational efficiency. Additionally, the proliferation of big data and the advent of digital transformation initiatives have accentuated the need for robust metadata management frameworks to manage the complexity of modern data ecosystems.

Another significant growth factor for the metadata management solutions market is the rising regulatory compliance requirements. Governments and regulatory bodies worldwide are enforcing stringent data protection laws and standards, such as GDPR in Europe and CCPA in California. Organizations are compelled to implement effective metadata management strategies to ensure compliance with these regulations, avoid hefty fines, and protect their reputation. Metadata management solutions offer the necessary tools and capabilities to track data lineage, enforce data privacy policies, and ensure data accuracy and consistency, thereby helping organizations mitigate compliance risks.

The increasing adoption of cloud-based solutions is also contributing to the market growth. Cloud-based metadata management solutions offer several advantages, including scalability, cost-efficiency, and ease of deployment. As businesses continue to migrate their data and applications to the cloud, the demand for cloud-based metadata management solutions is expected to rise. These solutions provide seamless integration with cloud data services, enabling organizations to manage and govern their data assets effectively in a cloud environment. Furthermore, the COVID-19 pandemic has accelerated the shift towards remote work, further driving the adoption of cloud-based metadata management solutions to support distributed and virtual teams.

In terms of regional outlook, North America is expected to dominate the metadata management solutions market during the forecast period. The region's strong presence of major technology companies, coupled with the early adoption of advanced data management practices, is driving market growth. Europe is also witnessing significant growth, primarily due to stringent data protection regulations and the increasing emphasis on data governance. The Asia Pacific region is anticipated to register the highest CAGR, driven by the rapid digital transformation initiatives and the growing importance of data-driven decision-making in emerging economies like China and India. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by the increasing adoption of metadata management solutions across various industries.

Component Analysis

The metadata management solutions market is segmented into software and services based on components. The software segment holds a significant market share and is expected to maintain its dominance throughout the forecast period. Metadata management software encompasses a wide range of tools and applications designed to catalog, manage, and govern metadata. These solutions provide functionalities such as metadata discovery, data lineage tracking, metadata cataloging, and metadata integration, enabling organizations to effectively manage their data assets. The increasing demand for automated and scalable metadata management solutions is driving the growth of the software segment.

Within the software segment, data governance and data quality tools are gaining traction. Organizations are increasingly adopting these tools to ensure data accuracy, consistency, and compliance with regulatory requirements. Data governance tools help establish data ownership, define data policies, and enforce data standards, while data quality tools facilitate data cleansing, validation, and enrichment. The integration of advanced technologies such as artificial intelligence and machine learning in metadata management software

The DCAT extension for CKAN enhances data portals by enabling the exposure and consumption of metadata using the DCAT vocabulary, facilitating interoperability with other data catalogs. It provides tools for serializing CKAN datasets as RDF documents and harvesting RDF data from external sources, promoting data sharing and reuse. The extension supports various DCAT Application Profiles, and includes features for adapting schemas, validating data, and integrating with search engines like Google Dataset Search. Key Features: DCAT Schemas: Offers pre-built CKAN schemas for common Application Profiles (DCAT AP v1, v2, and v3), which can be customized to align with site-specific requirements. These schemas include tailored form fields and validation rules to ensure DCAT compatibility. DCAT Endpoints: Exposes catalog datasets in different RDF serializations, allowing external systems to easily consume CKAN metadata in a standardized format. RDF Harvester: Enables the import of RDF serializations from other catalogs, automatically creating CKAN datasets based on the harvested metadata. This promotes data aggregation and discovery across different data sources. DCAT-CKAN Mapping: Establishes a base mapping between DCAT and CKAN datasets, facilitating bidirectional transformation of metadata. The mapping is compatible with DCAT-AP v1.1, v2.1, and v3. RDF Parser and Serializer: Includes an RDF parser for extracting CKAN dataset dictionaries from RDF serializations and an RDF serializer for transforming CKAN dataset metadata into different semantic formats. Both components are customizable through profiles. Command Line Interface (CLI): Provides a command-line interface for managing and interacting with the extension's features, such as harvesting and data transformation tasks. Google Dataset Search Integration: Offers support for indexing datasets in Google Dataset Search, improving the visibility of CKAN datasets to a wider audience. Technical Integration: The ckanext-dcat extension extends CKAN's functionality by adding new plugins for RDF harvesting and serialization, allowing users to expose and consume DCAT metadata through the portal and enabling dataset enrichment from external sources. This integration can be customized through profiles that define custom data mappings. Benefits & Impact: By implementing the DCAT extension, CKAN-based data portals can significantly improve their interoperability with other data catalogs and data repositories that support DCAT. This facilitates data sharing, reuse, and discovery, as well as improves the visibility of datasets through indexing in services like Google Dataset Search. The extension's built-in schemas and validation rules ensure that CKAN metadata conforms to DCAT standards, while the RDF harvester simplifies the process of importing data from external sources. Funded by organizations like the Government of Sweden, Vinnova, and FIWARE, the extension has been developed for production use cases and promotes a data-driven ecosystem.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

Description of the data in this data set: structure of the survey and pre-defined answers (if any) 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed} 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question) 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility} 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 8. How would you assess the value of the following data categories? 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)} 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)} 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company} 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”} 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

Format of the file .xls, .csv (for the first spreadsheet only), .odt

Licenses or restrictions CC-BY

The orleans extension for CKAN enhances dataset metadata by providing the capability to add and manage a dataset's geographical extent. By offering the functionality to define the spatial coverage of a dataset, this extension improves discoverability and usability, particularly for geospatial datasets. This enhanced metadata can aid users in understanding the geographic scope of the data before accessing it, improving data selection and application. Key Features: Dataset Extent Management: Enables administrators and data publishers to define the spatial extent, or geographic boundaries, of their datasets. This functionality ensures that users can easily understand the geographic coverage of the data being provided. Spatial Metadata Enrichment: Integrates spatial metadata directly into CKAN's dataset descriptions to improve the searchability and understanding of geospatial data holdings within the catalog. Geospatial Context: Adds valuable geospatial context to datasets, thereby improving the overall quality of metadata and allows users filter search based on geographical coverage. Technical Integration: Although the readme provides limited details regarding the exact technical integration, one can assume that, the orleans extension likely leverages CKAN's plugin architecture to introduce new fields or sections in the dataset editing form to accommodate the extent information. Benefits & Impact: Implementing the orleans extension can significantly improve the discoverability of spatially referenced datasets within a CKAN catalog, ensuring that users have an easy way to assess the geographical relevance of the data. This improvement will helps users to easily determine the relevance of a dataset before spending time downloading and processing it. Overall, adding dataset extent data through this extension enhances the utility of CKAN as a geospatial data catalog.

Xavvy fuel is the leading source for Fuel Station POI and Price data worldwide and specialized in data quality and enrichment. We provide high quality POI Data of gas stations about different fuel types for all European countries. One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs. Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new AdBlue stations or Truck pumps - our data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion. In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data. 130+ sources including governments, petroleum companies, fuel card providers and crowd sourcing enable xavvy to provide various information about AdBlue / DEF Stations in Europe.

Especially if you want to display information about AdBlue stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, processing procedures are continuously improved to increase data quality: • regular quality controls (e.g. via monitoring dashboards) • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Check out our other Data Offerings available and gain more valuable market insights on gas stations and AdBlue distribution directly from the experts!

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ...

25+ Fuel Types like • Regular • Mid-Grade • Premium • Diesel • DEF • CNG •...

30+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Pay at Pump •...

20+ Payment options • Cash • Visa • MasterCard • Fueling Cards • Google Pay • ...

Xavvy fuel is the leading source for Gas Station Location Data and Gasoline Price data worldwide and specialized in data quality and enrichment. Xavvy provides POI Data of gas stations at a high quality level for the United States. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station. The level of information to be provided is highly customizable. One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our gas station data and gasoline price data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, our processing procedures are continuously improved to increase data quality:

• regular quality controls • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Integrate the largest database of Retail Gas Station Location Data, Amenities and accurate Diesel and Gasoline Price Data in Europe and North America into your business. Check out our other Data Offerings available, and gain more valuable market insights on gas stations directly from the experts!