Metadata

Information on Creating Metadata for your Spatial Collaboration and Hosting Portal products.

Data Publishers are responsible for providing and maintaining appropriate metadata to ensure that users have all the information required to understand the dataset and assess whether it is fit for their requirements. This must include up to date contact details of the Data Custodian or Data Provider.

Providing standards-based metadata with your item helps people learn about the item and decide which item best meets their needs. In DCS Spatial Services’ Spatial Collaboration and Hosting Portals, the metadata is saved with the item it describes.

Metadata provides the means for discovering spatial information by identifying ‘what,’ ‘where,’ ‘who,’ ‘when,’ and ‘how’ the data behind the information is constructed. Metadata is the means to disclose what the spatial data describes, as well as how it should and can be used, along with any limitations and restrictions.

To assist in maintaining the standard of content published to the portal, Spatial Services have implemented the auto-injection of a metadata table into all new content created or exiting content that has had a change to the share status. This new template will have an enforcement on several fields as a minimum requirement to be completed before a new service can be shared. This is to ensure that the services published in the portal are supported with the minimum amount of information to be published with all new content.

Changes to Metadata collection in the Hosting and Collaboration Portal: -

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Get detailed insights into the current valuation of Metadata Management Solutions market size, including growth analysis, current market status and future market projections.

Dataset Card for text-descriptives-metadata

This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.

  Dataset Summary

This dataset contains:

A dataset configuration file conforming to the Argilla dataset format named argilla.yaml. This configuration file will be used to configure the dataset when using the… See the full description on the dataset page: https://huggingface.co/datasets/argilla/text-descriptives-metadata.

This dataset contains a standard template for representing the metadata of mineral spectral reference specimens 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, hazard declaration and physical storage. The template will be used to catalogue reference samples used for mineral spectral analysis (NVCL). 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: This template was built on the CMR rock metadata template (https://doi.org/10.25919/2prf-dk88). 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, mineral type, EPSG and hazard information to ensure consistency in metadata entry. The template also contains few metadata fields that are specific to mineral spectra samples like different analysis conducted for the samples (XRD, Whole-rock geochemical analysis, etc).

The global metadata management tools market was valued at USD 6.26 billion in 2020 and is expected to grow at a CAGR of 18.4% during 2021 - 2029.

Metadata form template for Tempe Open Data.

Analyze the market segmentation of the Metadata Management Tools industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.

The Enterprise Metadata Management (EMM) market is experiencing robust growth, projected to reach $6652.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.3% from 2025 to 2033. This expansion is driven by the increasing need for data governance, risk mitigation, and improved operational efficiency across diverse industries. Organizations are increasingly recognizing the strategic value of their data assets, necessitating comprehensive EMM solutions to ensure data quality, security, and compliance. The rising complexity of data landscapes, coupled with stringent regulatory requirements like GDPR and CCPA, are further fueling market demand. Key growth segments include Governance and Compliance Management, Risk Management, and Incident Management applications. The market's competitive landscape is populated by both established players like IBM, Informatica, and Oracle, and innovative emerging companies offering specialized solutions. The substantial investment in cloud-based EMM platforms and the growing adoption of AI and machine learning for metadata management are also contributing factors. North America currently dominates the EMM market, benefiting from early adoption and the presence of major technology companies. However, significant growth opportunities exist in the Asia-Pacific region, driven by rapid digitalization and expanding data volumes. Europe continues to witness steady growth, propelled by stringent data privacy regulations. The market is segmented by tools and services, offering a range of solutions catering to different organizational needs and budgets. Future market expansion will likely be shaped by advancements in data virtualization, semantic technologies, and the increasing integration of EMM solutions with other enterprise software systems. Continued focus on user experience and streamlined implementation will further enhance market adoption and penetration.

This file describes where to find the dataset used for this paper (PurpleAir and AQS) and the data fields used in the analysis. Contact the corresponding author for access to the code used to generate the dataset. This dataset is associated with the following publication: deSouza, P., K. Barkjohn, A. Clements, J. Lee, R. Kahn, and B. Crawford. An analysis of degradation in low-cost particulate matter sensors. Environmental Science: Atmospheres. Royal Society of Chemistry, Cambridge, UK, NA, (2023).

[218+ Pages Report] Metadata management tools market size & share will grow to USD 15.03 Billion by 2026, at a CAGR of 19% from 2021 to 2026.

Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012–2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets.

This dataset contains Zenodo's published open access records and communities metadata, including entries marked by the Zenodo staff as spam and deleted.

The datasets are gzipped compressed JSON-lines files, where each line is a JSON object representation of a Zenodo record or community.

Records dataset

Filename: zenodo_open_metadata_{ date of export }.jsonl.gz

Each object contains the terms: part_of, thesis, description, doi, meeting, imprint, references, recid, alternate_identifiers, resource_type, journal, related_identifiers, title, subjects, notes, creators, communities, access_right, keywords, contributors, publication_date

which correspond to the fields with the same name available in Zenodo's record JSON Schema at https://zenodo.org/schemas/records/record-v1.0.0.json.

In addition, some terms have been altered:

• The term files contains a list of dictionaries containing filetype, size, and filename only.
• The term license contains a short Zenodo ID of the license (e.g. "cc-by").

Communities dataset

Filename: zenodo_community_metadata_{ date of export }.jsonl.gz

Each object contains the terms: id, title, description, curation_policy, page

which correspond to the fields with the same name available in Zenodo's community creation form.

Notes for all datasets

For each object the term spam contains a boolean value, determining whether a given record/community was marked as spam content by Zenodo staff.

Some values for the top-level terms, which were missing in the metadata may contain a null value.

A smaller uncompressed random sample of 200 JSON lines is also included for each dataset to test and get familiar with the format without having to download the entire dataset.

This is a graphic representation of the data stewards based on PLSS Townships in PLSS areas. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In other implementations this may have been termed the alternate source, meaning alternate to the BLM. But in the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward.

This dataset contains the metadata of the datasets published in 101 Dataverse installations, information about the metadata blocks of 106 installations, and the lists of pre-defined licenses or dataset terms that depositors can apply to datasets in the 88 installations that were running versions of the Dataverse software that include the "multiple-license" feature. The data is useful for improving understandings about how certain Dataverse features and metadata fields are used and for learning about the quality of dataset and file-level metadata within and across Dataverse installations. How the metadata was downloaded The dataset metadata and metadata block JSON files were downloaded from each installation between August 25 and August 30, 2024 using a "get_dataverse_installations_metadata" function in a collection of Python functions at https://github.com/jggautier/dataverse-scripts/blob/main/dataverse_repository_curation_assistant/dataverse_repository_curation_assistant_functions.py. In order to get the metadata from installations that require an installation account API token to use certain Dataverse software APIs, I created a CSV file with two columns: one column named "hostname" listing each installation URL for which I was able to create an account and another column named "apikey" listing my accounts' API tokens. The Python script expects the CSV file and the listed API tokens to get metadata and other information from installations that require API tokens in order to use certain API endpoints. How the files are organized ├── csv_files_with_metadata_from_most_known_dataverse_installations │ ├── author_2024.08.25-2024.08.30.csv │ ├── contributor_2024.08.25-2024.08.30.csv │ ├── data_source_2024.08.25-2024.08.30.csv │ ├── ... │ └── topic_classification_2024.08.25-2024.08.30.csv ├── dataverse_json_metadata_from_each_known_dataverse_installation │ ├── Abacus_2024.08.26_15.52.42.zip │ ├── dataset_pids_Abacus_2024.08.26_15.52.42.csv │ ├── Dataverse_JSON_metadata_2024.08.26_15.52.42 │ ├── hdl_11272.1_AB2_0AQZNT_v1.0(latest_version).json │ ├── ... │ ├── metadatablocks_v5.9 │ ├── astrophysics_v5.9.json │ ├── biomedical_v5.9.json │ ├── citation_v5.9.json │ ├── ... │ ├── socialscience_v5.6.json │ ├── ACSS_Dataverse_2024.08.26_00.02.51.zip │ ├── ... │ └── Yale_Dataverse_2024.08.25_03.52.57.zip └── dataverse_installations_summary_2024.08.30.csv └── dataset_pids_from_most_known_dataverse_installations_2024.08.csv └── license_options_for_each_dataverse_installation_2024.08.28_14.42.54.csv └── metadatablocks_from_most_known_dataverse_installations_2024.08.30.csv This dataset contains two directories and four CSV files not in a directory. One directory, "csv_files_with_metadata_from_most_known_dataverse_installations", contains 20 CSV files that list the values of many of the metadata fields in the "Citation" metadata block and "Geospatial" metadata block of datasets in the 101 Dataverse installations. For example, author_2024.08.25-2024.08.30.csv contains the "Author" metadata for the latest versions of all published, non-deaccessioned datasets in 101 installations, with a column for each of the four child fields: author name, affiliation, identifier type, and identifier. The other directory, "dataverse_json_metadata_from_each_known_dataverse_installation", contains 106 zip files, one zip file for each of the 106 Dataverse installations whose sites were functioning when I attempted to collect their metadata. Each zip file contains a directory with JSON files that have information about the installation's metadata fields, such as the field names and how they're organized. For installations that had published datasets, and I was able to use Dataverse APIs to download the dataset metadata, the zip file also contains: A CSV file listing information about the datasets published in the installation, including a column to indicate if the Python script was able to download the Dataverse JSON metadata for each dataset. A directory of JSON files that contain the metadata of the installation's published, non-deaccessioned dataset versions in the Dataverse JSON metadata schema. The dataverse_installations_summary_2024.08.30.csv file contains information about each installation, including its name, URL, Dataverse software version, and counts of dataset metadata included and not included in this dataset. The dataset_pids_from_most_known_dataverse_installations_2024.08.csv file contains the dataset PIDs of published datasets in 101 Dataverse installations, with a column to indicate if the Python script was able to download the dataset's metadata. It's a union of all "dataset_pids_....csv" files in each of the 101 zip files in the dataverse_json_metadata_from_each_known_dataverse_installation directory. The license_options_for_each_dataverse_installation_2024.08.28_14.42.54.csv file contains information about the licenses and...

This publication is an introductory/summary paper to a virtual special issue of the journal Science of the Total Environment titled: Advances in Mercury Research--Reviews of Recent Advances in Mercury Research and Understanding the Biogeochemical Cycle. The special issue contained 11 Research Articles that focused on different aspects of mercury cycling. All these papers were “review papers” and relied on synthesizing conclusions from existing published research and did not involve any direct data collection. The Gustin et al, 2020 paper (which is associated with this Metadata Statement) provides an introduction to the journal’s special issue and briefly summarizes the primary conclusions from the other 11 papers. Therefore, there are not any datasets associated with this publication. This dataset is not publicly accessible because: This publication is an introductory/summary paper to a virtual special issue of the journal Science of the Total Environment titled: Advances in Mercury Research--Reviews of Recent Advances in Mercury Research and Understanding the Biogeochemical Cycle. The special issue contained 11 Research Articles that focused on different aspects of mercury cycling. All these papers were “review papers” and relied on synthesizing conclusions from existing published research and did not involve any direct data collection. The Gustin et al, 2020 paper (which is associated with this Metadata Statement) provides an introduction to the journal’s special issue and briefly summarizes the primary conclusions from the other 11 papers. Therefore, there are not any datasets associated with this publication. It can be accessed through the following means: This paper does not include any data. Format: This paper does not include any data. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide i ...

This is a graphic representation of the data stewards based on PLSS Townships in PLSS areas within the BLM Administrative State of Wyoming. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In other implementations this may have been termed the alternate source, meaning alternate to the BLM. But in the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward. These are not the official representations of the lines marked by the survey, please contact Wyoming Cadastral Survey.

Dryad is a general-purpose curated repository for data underlying scholarly publications. Dryad’s metadata framework is supported by a Dublin Core Application Profile (DCAP, hereafter referred to as application profile). This paper examines the evolution of Dryad’s application profile, which has been revised over time, in an operational system, serving day-to-day needs of stakeholders. We model the relationships between data packages and data files over time, from its initial implementation in 2007 to its current practice, version 3.2, and present a crosswalk analysis. Results covering versions 1.0 to 3.0 show an increase in the number of metadata elements used to describe Dryad’s data objects in Dryad. Results also confirm that Version 3.0, which envisioned separate metadata element sets for data package, data files, and publication metadata, was never fully realized due to constraints in Dryad system architecture. Version 3.1 subsequently reduced the number of metadata elements captured by recombining the publication and data package element sets. This paper documents a real world application profile implemented in an operational system, noting practical system and infrastructure constraints. Finally, the analysis presented informs an ongoing effort to update the application profile to support Dryad's diverse and expanding community of stakeholders.

This dataset contains key characteristics about the data described in the Data Descriptor The AgeGuess database, an open online resource on chronological and perceived ages of people aged 5-100. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON formatVersioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

This paper reports on a study exploring 'metadata capital' acquired via metadata reuse. Collaborative modeling and content analysis methods were used to study metadata capital in the Dryad data repository. A sample of 20 cases for two Dryad metadata workflows (Case A and Case B) consisting of 100 instantiations (60 metadata objects, 40 metadata activities) was analyzed. Results indicate that Dryad's overall workflow builds metadata capital, with the total metadata reuse at 50% or greater for 8 of 12 metadata properties, and 5 of these 8 properties showing reuse at 80% or higher. Metadata reuse is frequent for basic bibliographic properties (e.g., author, title, subject), although it is limited or absent for more complex scientific properties (e.g., taxon, spatial, and temporal information). This paper provides background context, reports the research approach and findings, and considers research implications and system design priorities that may contribute to metadata capital—long term.