An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.

Sharing descriptive Metadata is the first essential step towards Open Scientific Data. With this in mind, Maggot was specifically designed to annotate datasets by creating a metadata file to attach to the storage space. Indeed, it allows users to easily add descriptive metadata to datasets produced within a collective of people (research unit, platform, multi-partner project, etc.). This approach fits perfectly into a data management plan as it addresses the issues of data organization and documentation, data storage and frictionless metadata sharing within this same collective and beyond. Main features of Maggot The main functionalities of Maggot were established according to a well-defined need (See Background) Documente with Metadata your datasets produced within a collective of people, thus making it possible : o answer certain questions of the Data Management Plan (DMP) concerning the organization, documentation, storage and sharing of data in the data storage space, to meet certain data and metadata requirements, listed for example by the Open Research Europe in accordance with the FAIR principles. Search datasets by their metadata : Indeed, the descriptive metadata thus produced can be associated with the corresponding data directly in the storage space then it is possible to perform a search on the metadata in order to find one or more sets of data. Only descriptive metadata is accessible by default. Publish the metadata of datasets along with their data files into an Europe-approved repository

Dataset Card for Macappstore Applications Metadata

Mac App Store Applications Metadata sourced by the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This data aims to cover our internal company research needs and start collecting and sharing the macOS app dataset since we have yet to find a suitable existing one. Full application metadata was sourced by the public iTunes search API for the US, Germany, and Ukraine… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-metadata.

The main functionalities of Maggot were established according to a well-defined need (See Background) Documente with Metadata your datasets produced within a collective of people, thus making it possible : o answer certain questions of the Data Management Plan (DMP) concerning the organization, documentation, storage and sharing of data in the data storage space, to meet certain data and metadata requirements, listed for example by the Open Research Europe in accordance with the FAIR principles. Search datasets by their metadata : Indeed, the descriptive metadata thus produced can be associated with the corresponding data directly in the storage space then it is possible to perform a search on the metadata in order to find one or more sets of data. Only descriptive metadata is accessible by default. Publish the metadata of datasets along with their data files into an Europe-approved repository PHP, 7.4.33 Mongodb, 6.0.14 Python, 3.8.10 Docker, 20.10.12

This is the annotated data which is used in evaluation of REMAP.

This file contains a description of the metadata for each of the serum samples that were evaluated in the project.

This dataset was extracted from a set of metadata files harvested from the DataCite metadata store (http://search.datacite.org/ui) during December 2015. Metadata records for items with a resourceType of dataset were collected. 1,647,949 total records were collected. This dataset contains four files: 1) readme.txt: a readme file. 2) language-results.csv: A CSV file containing three columns: DOI, DOI prefix, and language text contents 3) language-counts.csv: A CSV file containing counts for unique language text content values. 4) language-grouped-counts.txt: A text file containing the results of manually grouping these language codes.

Stores physical and logical information about relational databases and record structures to assist in data identification and management.

Environmental Data

Knowing when and where an image was taken is really important. OK, you can log this in your head:But how reliable is your memory? If you visit a place multiple times there is potential for confusion and if you take lots of similar pictures of similar features in different locations, how confident are you of remembering al the details?Digital photographs have an advantage over film based images in that the camera creates a metadata file to store "useful" information.

Metadata Management Services Market Outlook

The global metadata management services market size is projected to grow from USD 4.5 billion in 2023 to an estimated USD 9.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.3% over the forecast period. This growth is driven by the increasing demand for data governance and the need for consistent data quality across various industries. As organizations continue to grapple with vast amounts of data, the ability to effectively manage and utilize metadata is becoming increasingly critical, prompting significant investments in metadata management solutions.

One of the primary drivers of the growth in the metadata management services market is the burgeoning need for effective data governance frameworks. As data becomes a central asset for businesses, ensuring that data is accurate, consistent, and secure is imperative. Metadata management solutions facilitate the alignment of data with business objectives and regulatory requirements, enhancing decision-making and operational efficiency. Additionally, the increasing stringency of data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, is compelling organizations to adopt robust metadata management practices to ensure compliance.

Another significant growth factor is the rise of cloud computing, which has revolutionized the way businesses manage and store data. The shift towards cloud-based solutions has increased the need for sophisticated metadata management services that can handle distributed data environments. Cloud platforms offer scalable and flexible deployment options that are particularly appealing to organizations looking to streamline their operations and reduce IT overheads. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) technologies into metadata management solutions is further enhancing their capabilities, allowing for more advanced data analytics and automation of routine processes.

The proliferation of big data and the Internet of Things (IoT) is also contributing to the growth of the metadata management services market. As the volume, variety, and velocity of data continue to increase, organizations are seeking advanced solutions to manage and derive value from this data. Metadata management services provide the necessary tools to organize and interpret large datasets, enabling businesses to gain insights and drive innovation. This demand is particularly pronounced in sectors such as finance, healthcare, and retail, where real-time data analysis can lead to competitive advantages.

From a regional perspective, North America holds the largest share of the metadata management services market, owing to the presence of a large number of technology providers and early adopters in the region. The market is also experiencing significant growth in the Asia Pacific region, driven by advancements in digital infrastructure and an increasing focus on data-driven decision-making across industries. Furthermore, the European market is expected to see considerable growth due to stringent data privacy regulations and the rapid adoption of cloud technologies. Each of these regions presents unique opportunities and challenges for market players, influencing their strategic initiatives and investments.

Component Analysis

The metadata management services market is segmented by components, primarily into software and services. The software segment encompasses a variety of solutions designed to automate and streamline metadata management processes. These include tools for data cataloging, data quality, and data lineage, which are essential for creating a comprehensive metadata repository. The demand for advanced software solutions is being driven by the need to handle increasingly complex data environments and the availability of new technologies that enhance data analysis capabilities, such as artificial intelligence and machine learning. Vendors are continuously enhancing their software offerings to improve functionality and user experience, which in turn fuels market growth.

Services in the metadata management market include consulting, implementation, and support services that help organizations effectively deploy and manage metadata solutions. Consulting services assist businesses in understanding their metadata management needs and developing strategies to optimize data usage. Implementation services involve the setup and configuration of metadata solutions, ensuring they align with the organization's data

The size of the Metadata Management Tools Market market was valued at USD 8.05 billion in 2023 and is projected to reach USD 30.22 billion by 2032, with an expected CAGR of 20.8 % during the forecast period. The Metadata Management Tools Market covers software applications used for creation, storage, governance, analysis, and tracking of metadata, which is information about some other information. The above tools are critical in the management, quality, and compliance of information in organizations so that the businesses can tap into their resources effectively. Some of the meta data management uses are data integration, data warehousing, business intelligence and regulators. Some current trends are the use of artificial intelligence and machine learning for the automation of the metadata tagging process, shift to the cloud in order to scale up easily, and, most significantly, the importance of data confidentiality and security within metadata management strategies. It is expanding further because numerous businesses today are placing a higher emphasis on analytics and going digital.

Git LFS Support: Integrates with Git LFS to manage large resource files effectively, preventing repository bloat. Extensible Backend Support: Aims to support additional Git services like GitLab in future releases. Technical Integration: The extension operates by adding plugins to CKAN (gitdatahubpackage and gitdatahubresource). These plugins hook into CKAN's workflow to automatically write dataset and resource metadata to the configured Git repository. The extension requires configuration via CKAN's .ini file to enable the plugins and provide necessary settings, such as the GitHub API access token. Benefits & Impact: Utilizing the gitdatahub extension provides version control for CKAN metadata, enabling administrators to track changes to datasets and resources over time. The storage of metadata in the Frictionless Data format promotes interoperability and data portability, due to well-defined open standards. Use of Git provides an audit trail and allows others to collaborate and contribute. The extension is helpful when organizations need to keep copy of the metadata outside of CKAN and want to provide an audit trail for their data.

This dataset contains a standard template for representing the metadata of rock specimens (e.g., core, microanalysis, hand grab) in the CSIRO Mineral Resources Discovery program. The template includes core properties of samples such as their name, identifier, type, and location, as well as associated metadata such as project, drilling contexts, hazard declaration and physical storage. The template will be used to catalogue legacy and specimens systematically collected through mineral exploration projects. It has been developed iteratively, revised, and improved based on feedback from researchers and lab technicians. This standardized template can prevent duplicate sample metadata entry and lower metadata redundancy, thereby improving the program's physical sample curation and discovery. Lineage: The template includes a readme section summarising all the metadata fields, including their requirements and definitions. The template incorporates several established controlled terms representing, e.g., sample type, rock type, drill type, EPSG and hazard information to ensure consistency in metadata entry.

This file contains metadata describing the bio specimen that was included on each peptide array, its isolation and extraction history, as well as the file name for the raw data that was collected from each bio specimen.

What is DICOM

DICOM (Digital Imaging and Communications in Medicine) is a standard format used to store and transmit medical images and related information in healthcare settings. It's a widely used format for various types of medical images, including X-rays, MRIs, CT scans, ultrasounds, and more. DICOM files typically contain a wealth of information beyond just the image pixels. This extra data would be wonderful for feature engineering. Here's an overview of the data possibly stored in a DICOM image format (the original RSNA ATD dataset has most likely been purged of PII, and majority of these fields are not present):

1. Patient Information (Patient's name, Patient's ID, Patient's date of birth etc.)

2. Study Information (Study description, Study date and time, Study ID etc.)

3. Series Information:

   • Series description
   • Modality (e.g., CT, MRI, X-ray, ultrasound)
   • Series instance UID (a unique identifier for the series)
   • Number of images in the series
   • Image orientation and position information

4. Image Information:

   • Image type (e.g., original, derived, etc.)
   • Photometric interpretation (how pixel values represent image information, e.g., grayscale, RGB)
   • Rows and columns (image dimensions)
   • Pixel spacing (physical size of each pixel)
   • Bits allocated and bits stored (bit depth of pixel values)
   • High bit (the most significant bit)
   • Windowing and leveling settings for image display
   • Rescale intercept and slope (used for converting pixel values to physical units)
   • Image orientation (patient positioning)

5. Image Acquisition Details:

   • Exposure parameters (e.g., radiation dose in radiography, MRI sequence parameters)
   • Image acquisition date and time
   • Equipment information (e.g., machine make and model)
   • Image acquisition technique (e.g., pulse sequence in MRI)
   • Image Annotations and Markings:

6. Image Pixel Data: The actual image pixel values, which can be 2D or 3D depending on the image type Encoded in a format such as raw pixel data or compressed image data (e.g., JPEG, JPEG2000)

How can this Dataset be used?

1. Feature Engineering
2. 3D Visualization of Scan series
3. Anomaly Detection

Columns in the Dataset

Here's an explanation of each of the fields in the dataset:

1. SOP Instance UID (Unique Identifier):

   • A globally unique identifier assigned to each instance (e.g., an individual image or a series) within a DICOM study. It helps identify and distinguish different instances.

2. Content Date:

   • The date when the image or data was created or acquired. It's typically in the format YYYYMMDD (year, month, day).

3. Content Time:

   • The time when the image or data was created or acquired. It's typically in the format HHMMSS.FFFFFF (hour, minute, second, fraction of a second).

4. Patient ID:

   • A unique identifier for the patient, often used to link different studies and images to the same patient.

5. Slice Thickness:

   • The thickness of an image slice in millimeters, relevant in three-dimensional imaging modalities like CT scans.

6. KVP (Kilovolt Peak):

   • The peak voltage of the X-ray machine used to acquire the image. It affects the quality and contrast of the image.

7. Patient Position:

   • The position of the patient during image acquisition, such as supine, prone, standing, etc.

8. Study Instance UID:

   • A unique identifier assigned to each study, which may consist of multiple series and images related to a specific medical examination or procedure.

9. Series Instance UID:

   • A unique identifier assigned to each series within a study. A series contains a group of related images.

10. Series Number:

    • An integer identifier that indicates the position of the series within the study.

11. Instance Number:

    • An integer identifier that indicates the position of the image or data instance within a series.

12. Image Position (Patient):

    • The position of the image slice within the patient's anatomy, typically defined by three coordinates (x, y, z) in millimeters.

13. Image Orientation (Patient):

    • The orientation of the image with respect to the patient's anatomy, typically defined by six parameters that describe the direction cosines of the rows and columns.

14. Frame of Reference UID:

    • An identifier that establishes a coordinate system for images within a study, enabling proper alignment and orientation of images in multi-modality studies.

15. Samples per Pixel:

    • The number of data samples (e.g., pixels) per image pixel.

16. Photometric Interpretation:

    • Describes how pixel data is interpreted for display, such as grayscale, RGB color, o...

• DEPRECATION NOTE - Please, keep in mind that this namespace has been superseeded by ‘gold’ prefix at https://registry.identifiers.org/registry/gold, and this namespace is kept here for support to already existing citations, new ones would need to use the pointed ‘gold’ namespace.

The GOLD (Genomes OnLine Database)is a resource for centralized monitoring of genome and metagenome projects worldwide. It stores information on complete and ongoing projects, along with their associated metadata. This collection references metadata associated with samples.

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Data Catalog Market Size 2025-2029

The data catalog market size is forecast to increase by USD 5.03 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for self-service analytics. With the proliferation of big data and the need for organizations to derive valuable insights from their data, there is a growing emphasis on having easily accessible and searchable catalogs. Another key trend in the market is the emergence of data mesh architecture, which aims to distribute data ownership and management across the organization. However, maintaining catalog accuracy over time poses a significant challenge. As data volumes continue to grow and change rapidly, ensuring that catalogs remain up-to-date and accurate becomes increasingly difficult.
Companies seeking to capitalize on the opportunities presented by the market must invest in robust catalog management solutions and adopt best practices for data governance. At the same time, they must also address the challenge of maintaining catalog accuracy by implementing automated data discovery and catalog update processes. By doing so, they can ensure that their catalogs remain a valuable asset, enabling efficient data access and driving better business outcomes.

What will be the Size of the Data Catalog Market during the forecast period?

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The market continues to evolve, driven by the increasing importance of data-driven decision making across various sectors. Data standardization methods, such as the Data Mesh framework, play a crucial role in ensuring consistency and interoperability in data management. A notable example is a financial services company that achieved a 25% increase in sales by implementing a standardized data asset inventory using master data management and reference data management techniques. Industry growth in data cataloging is expected to reach 20% annually, fueled by the adoption of data lake architecture, data model optimization, and metadata schema design. Data version control, data access control, semantic enrichment, and data lineage tracking are essential components of data cataloging software, enabling effective data governance policies and metadata management.
Data anonymization methods, data cleansing processes, and data observability tools are integral to maintaining data quality. Data integration platforms employ data quality rules, entity resolution techniques, and data usage monitoring to ensure data accuracy and consistency. Data profiling techniques and data transformation pipelines facilitate the conversion of raw data into valuable insights. Data virtualization, data warehouse design, and data mapping tools enable seamless access to data, while knowledge graph creation and data governance policies foster collaboration and data sharing.

How is this Data Catalog Industry segmented?

The data catalog industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Deployment

  Cloud
  On-premises


Type

  Technical metadata
  Business metadata
  Operational metadata


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The Solutions segment is estimated to witness significant growth during the forecast period. Data catalog solutions have gained significant traction in the US business landscape, addressing the pressing needs of data discovery, governance, collaboration, and data lifecycle management. According to recent studies, over 35% of organizations have adopted data catalog solutions, a testament to their value in streamlining data management processes. Looking ahead, industry experts anticipate that the demand for data catalog solutions will continue to grow, with expectations of a 30% increase in market penetration in the coming years. These solutions enable users to efficiently search and discover relevant datasets for their analytical and reporting requirements, reducing the time spent locating data and encouraging data reuse. Metadata plays a crucial role in understanding unstructured data, which is increasingly prevalent in sectors like healthcare and e-commerce.

Centralized metadata storage offers detailed information about datasets, including source, schema, data quality, and lineage, enhancing data understanding, facilitating governance, and ensuring context for effective data utilization. Data catalog solutions are a crucial component of modern data management and analytics ecosystems, continually evolving to meet the dynamic needs of

Point of Interest (POI) is defined as an entity (such as a business) at a ground location (point) which may be (of interest). We provide high-quality POI data that is fresh, consistent, customizable, easy to use and with high-density coverage for all countries of the world.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

A new POI comes into existence. It could be a bar, a stadium, a museum, a restaurant, a cinema, or store, etc.. In today's interconnected world its information will appear very quickly in social media, pictures, websites, press releases. Soon after that, our systems will pick it up.

POI Data is in constant flux. Every minute worldwide over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist. And over 94% of all businesses have a public online presence of some kind tracking such changes. When a business changes, their website and social media presence will change too. We'll then extract and merge the new information, thus creating the most accurate and up-to-date business information dataset across the globe.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via our data update pipeline.

Customers requiring regularly updated datasets may subscribe to our Annual subscription plans. Our data is continuously being refreshed, therefore subscription plans are recommended for those who need the most up to date data. The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

Data samples may be downloaded at https://store.poidata.xyz/us

This dataset is used for benchmarking five spatially-enabled RDF stores, i.e. RDF4J, GeoSPARQL-Jena, VIrtuoso, Stardog, and GraphDB. It can also be used for further testing other stores or the upgraded stores. The dataset is GeoSPARQL-compliant and has 1,068 spatial objects (including 88 polygons, 853 polylines, and 127 points).