The Metadata Management Tools market is experiencing robust growth, driven by the increasing volume and complexity of data across various industries. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $40 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based solutions provides scalability and cost-effectiveness, attracting businesses of all sizes. Secondly, the stringent regulatory compliance needs across sectors like BFSI and healthcare necessitate robust metadata management for data governance and security. Furthermore, the growing demand for data-driven decision-making and advanced analytics increases the reliance on accurate and readily accessible metadata. Key trends include the integration of AI and machine learning for automated metadata discovery and classification, and the increasing demand for solutions offering enhanced data lineage capabilities. While the market faces restraints like the complexity of implementation and the need for skilled professionals, the overall positive market outlook is supported by continuous innovation and increasing enterprise awareness of the value proposition of effective metadata management. The market is segmented by deployment (cloud-based and on-premise) and application (BFSI, retail, medical, media, and others). Major players such as Oracle, SAP, IBM, and Informatica dominate the market, while several emerging players are also vying for market share through innovative solutions. The North American region currently holds the largest market share, followed by Europe and Asia Pacific. The competitive landscape is marked by both established players and innovative startups. Established players leverage their existing customer base and extensive product portfolios, while emerging companies often focus on niche solutions and advanced technologies. The market is witnessing increased mergers and acquisitions, strategic partnerships, and product advancements, indicative of a dynamic and competitive landscape. Future growth hinges on the ability of vendors to adapt to the evolving technological landscape, meet the growing need for data security and compliance, and provide user-friendly, scalable, and cost-effective solutions. The focus on data quality, interoperability, and governance will continue to shape the development and adoption of metadata management tools across industries. Geographical expansion, especially into developing economies, presents a significant opportunity for market growth.

The dataset consists of public domain acute and chronic toxicity and chemistry data for algal species. Data are accessible at: https://envirotoxdatabase.org/ Data include algal species, chemical identification, and the concentrations that do and do not affect algal growth.

The Metadata Management Services market is experiencing robust growth, driven by the increasing volume and complexity of data across organizations. The market's expansion is fueled by the urgent need for better data governance, improved data quality, and enhanced compliance with regulations like GDPR and CCPA. Organizations are increasingly adopting cloud-based solutions and leveraging AI/ML capabilities within their metadata management strategies to streamline operations and gain valuable insights from their data assets. The market's size in 2025 is estimated at $5 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is propelled by the rising adoption of metadata management solutions across various industries, including finance, healthcare, and retail, which are prioritizing data-driven decision-making and operational efficiency. Key players like IBM, Oracle, and SAP are leading the market with established solutions, while smaller, specialized vendors are innovating with niche offerings and driving competition. The market segmentation shows a strong preference towards cloud-based solutions due to their scalability, flexibility, and cost-effectiveness. While the market presents significant opportunities, challenges remain. The complexity of implementing and integrating metadata management solutions can be a significant hurdle, alongside concerns regarding data security and privacy. Moreover, the lack of skilled professionals capable of managing and interpreting metadata can hinder widespread adoption. However, these challenges are being addressed through improved user-friendly interfaces, enhanced security features, and the growth of training and certification programs within the field. The forecast period (2025-2033) anticipates continued growth, driven by the increasing adoption of advanced analytics, big data, and the Internet of Things (IoT), all of which generate substantial metadata that requires effective management. This continued growth is expected across all segments and regions, but particularly strong in North America and Europe due to advanced digital transformation initiatives and stringent regulatory environments.

This dataset outlines a proposed set of core, minimal metadata elements that can be used to describe biomedical datasets, such as those resulting from research funded by the National Institutes of Health. It can inform efforts to better catalog or index such data to improve discoverability. The proposed metadata elements are based on an analysis of the metadata schemas used in a set of NIH-supported data sharing repositories. Common elements from these data repositories were identified, mapped to existing data-specific metadata standards from to existing multidisciplinary data repositories, DataCite and Dryad, and compared with metadata used in MEDLINE records to establish a sustainable and integrated metadata schema. From the mappings, we developed a preliminary set of minimal metadata elements that can be used to describe NIH-funded datasets. Please see the readme file for more details about the individual sheets within the spreadsheet.

Market Size and Growth: The global Metadata Management Software market was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033, reaching USD XXX million by the end of the forecast period. The increasing demand for efficient and accurate data management, coupled with the growing adoption of cloud-based solutions, are key drivers of this growth. The market is segmented by application (data governance, data integration, data quality, data security, and others) and type (structured, unstructured, and semi-structured). North America and Europe are currently the dominant regional markets, while Asia Pacific is expected to witness significant growth in the coming years. Key Trends and Challenges: One of the major trends in the Metadata Management Software market is the rise of artificial intelligence (AI) and machine learning (ML). AI-powered tools can automate metadata extraction, classification, and analysis tasks, reducing manual effort and improving accuracy. Another trend is the adoption of semantic technologies, which allow organizations to create more meaningful connections between different types of data. However, challenges such as data privacy and security concerns, as well as the lack of skilled professionals, could hinder market growth.

This file provides a metadata mapping between the Government of Canada’s Open Data Metadata Element Set and Canadian Provincial and Territorial Open Data Metadata Element Sets, where applicable. This was completed as part of a commitment made in the Government of Canada’s 4th National Action Plan, 10.6 Implement a pilot project to move toward cross-jurisdictional common data standards in line with the International Open Data Charter and other international standards – A Cross-jurisdictional metadata mapping is completed with a common set of core elements. Metadata elements were collected from open data portal throughout Canada, and this metadata mapping was completed in collaboration from the contributing provinces and territories.

The ESS-DIVE sample identifiers and metadata reporting format primarily follows the System for Earth Sample Registration (SESAR) Global Sample Number (IGSN) guide and template, with modifications to address Environmental Systems Science (ESS) sample needs and practicalities (IGSN-ESS). IGSNs are associated with standardized metadata to characterize a variety of different sample types (e.g. object type, material) and describe sample collection details (e.g. latitude, longitude, environmental context, date, collection method). Globally unique sample identifiers, particularly IGSNs, facilitate sample discovery, tracking, and reuse; they are especially useful when sample data is shared with collaborators, sent to different laboratories or user facilities for analyses, or distributed in different data files, datasets, and/or publications. To develop recommendations for multidisciplinary ecosystem and environmental sciences, we first conducted research on related sample standards and templates. We provide a comparison of existing sample reporting conventions, which includes mapping metadata elements across existing standards and Environment Ontology (ENVO) terms for sample object types and environmental materials. We worked with eight U.S. Department of Energy (DOE) funded projects, including those from Terrestrial Ecosystem Science and Subsurface Biogeochemical Research Scientific Focus Areas. Project scientists tested the process of registering samples for IGSNs and associated metadata in workflows for multidisciplinary ecosystem sciences.more » We provide modified IGSN metadata guidelines to account for needs of a variety of related biological and environmental samples. While generally following the IGSN core descriptive metadata schema, we provide recommendations for extending sample type terms, and connecting to related templates geared towards biodiversity (Darwin Core) and genomic (Minimum Information about any Sequence, MIxS) samples and specimens. ESS-DIVE recommends registering samples for IGSNs through SESAR, and we include instructions for registration using the IGSN-ESS guidelines. Our resulting sample reporting guidelines, template (IGSN-ESS), and identifier approach can be used by any researcher with sample data for ecosystem sciences.« less

According to Cognitive Market Research, the global enterprise metadata management market size is USD 7.85 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 24.1% from 2024 to 2031. Market Dynamics of Enterprise Metadata Management Market

Key Drivers for Enterprise Metadata Management Market

Rapidly expanding data sets- The market growth is fueled by enterprise metadata management. Enterprises need to manage and understand their massive and varied datasets as the amount of data generated by these entities continues to grow at an exponential rate. The management of structured and unstructured data is becoming more complicated as organizations gather massive volumes of data from many sources. Enterprise metadata management is crucial for comprehending data context, linkages, and usage; enterprise metadata management offers a framework for organizing, characterizing, and controlling data using metadata. Moreover, improved data quality, easier data integration, and system-wide consistency are all results of well-managed metadata. Better decision-making and operational efficiency can be achieved when firms use enterprise metadata management because it increases data discoverability, streamlines data processes, and supports advanced analytics.
The demand for enterprise metadata management is being driven by these markets becoming more popular because of the growth of big data and advanced analytics tools.

Key Restraints for Enterprise Metadata Management Market

The enterprise metadata management industry is restricted due to a high implementation cost.
The implementation and maintenance of enterprise metadata management solutions can be impeded by a lack of trained specialists in this industry.

za Introduction of the Enterprise Metadata Management Market

Enterprise metadata management is the process of managing all of an organization’s information. Metadata is information about other data that gives it organization, meaning, and context. Better management of data, following the rules, and better decisions are all made easier by enterprise metadata management, which makes sure that data is correctly defined and easy to find. The necessity for improved data governance and strict adherence to regulations is mostly driving the global enterprise metadata management market. The demand for enterprise metadata management is also being propelled by the increasingly digital landscape and the widespread use of advanced analytics. In addition, because it aids in managing and securing the metadata created and stored, blockchain technology is gaining traction across many industries, opening up enormous possibilities for enterprise metadata management. As a result, there will likely be a meteoric rise in the business metadata management industry. Issues with data consistency across numerous channels provide a challenge for both business users and IT departments in the enterprise metadata management market.

The monitor_and_extract_metadata.py script is designed to monitor a specified parent folder for new subfolders containing a Result.xml file. It extracts selected metadata from the Result.xml file and saves this metadata in both JSON and XML formats within the same subfolder.

In June 2019, the U.S. Geological Survey Maryland-Delaware-District of Columbia Water Science Center (MD-DE-DC WSC) team began to collect and inventory available information on toxic contaminants within the Chesapeake Bay Watershed. State agencies were contacted to determine available data. Also, the National Water Information System (NWIS) and National Water Quality Database (NWQD) were queried to gather relevant data for the compilation. The resulting tables contain records for available sites where specific analyte groups, Hg (mercury), PCB (polychlorinated biphenyls), or pesticides, have been collected with appropriate supplemental metadata including media, method, time frame, and frequency of collection. Sample results span 1972-2019. Files included in the data release: Basic_Table.csv Detailed_Table.csv NWIS_PCodes.csv State_Result_Totals.csv NWIS_Result_Totals.csv

Research data management (RDM) includes people with different needs, specific scientific contexts, and diverse requirements. The description of data is a big RDM challenge. Metadata plays an essential role, allowing the inclusion of essential information for the interpretation of data, enhances the reuse of data and its preservation. The establishment of metadata models can facilitate the process of description and contribute to an improvement in the quality of metadata. When we talk about image data, the task is even more difficult, as there are no explicit recommendations to guide image management. Taking all of this into account, in this dataset, we present a proposal for a metadata model for image description. We also developed controlled vocabularies for some descriptors. These vocabularies aim to improve the image description process, facilitate metadata model interpretation, and reduce the time and effort devoted to data description.

ABSTRACT The contemporary scenariomarked by the informational explosion it is essential that highly distributed information environments such as the Web, share and collaborate with organized and structured data and information. This reality has challenged professionals from different fields of knowledge, especially the area of Information Science to seek solutions to the informational treatment of such environments in order to ensure efficient search and retrieval of informational resources. Elucidated that the treatment of the information resource requires a description of form and content readable by machines with human understandable results, and it also meets the requirements of interoperability between informational environments. The objective is to check the relational dimension between the instruments for the representation of data and information, such as ontology and metadata, regarded as pillars for modeling, structuring catalogs and bibliographic systems. Through an exploratory and descriptive methodology interdisciplinary in the areas of Information Science and Computer Science are identified conceptual elements in order to point out the need for the construction of structured digital information environments and standardized. The use of ontology and metadata synergistically presents vital importance for the representation and the description of bibliographic resources in digital informational environments currently, providing a modeling of bibliographic catalogs and semantic interoperability between different systems and platforms.

The global Enterprise Metadata Management (EMM) market is anticipated to surge at a CAGR of 21.5% from 2025 to 2033, reaching a value of 8003.4 million by 2033. This growth is attributed to the increasing adoption of cloud-based applications, the need for data governance, and the rising demand for data privacy and security. EMM helps organizations manage and govern their metadata across various systems, enabling them to gain insights from their data and improve decision-making. The market is segmented by application, type, and region. The financial, retail, and medical sectors are the major users of EMM solutions. On-premise and cloud-based deployments are the two primary types of EMM. The cloud segment is expected to witness significant growth due to its scalability, flexibility, and cost-effectiveness. North America and Europe are the dominant regions in the market, while Asia Pacific is expected to show promising growth in the coming years. Key players in the EMM market include Oracle, Informatica, Collibra, IBM, and SAP. These companies are focusing on innovation and strategic partnerships to gain a competitive edge and cater to the growing demands of organizations.

Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation

Introduction

This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project DeeperSense (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. Parts of the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the FAIR principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately.

Locations and sensors

The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The examples/ directory holds a typical output image for each sensor at each available location.

Data volume per session

Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session:

Session dates Location Number of datasets Total duration of datasets [h] Total logfile size [GB] Number of images Total image size [GB]

2021-08-09 - 2021-08-12 Maritime Exploration Hall at DFKI RIC Bremen 52 10.8 28.8 389’047 88.1

2022-02-07 - 2022-02-08 Maritime Exploration Hall at DFKI RIC Bremen 35 4.4 54.1 629’626 62.3

2022-04-26 - 2022-04-28 Chalk Lake Hemmoor 52 8.1 133.6 1’114’281 97.8

2022-06-28 - 2022-06-29 Tank Wash Basin Neu-Ulm 42 6.7 144.2 824’969 26.9

2023-04-26 - 2023-04-27 Maritime Exploration Hall at DFKI RIC Bremen 55 7.4 141.9 739’613 9.6

2023-09-01 - 2023-09-02 Lake Starnberg 19 2.9 40.1 217’385 2.3

255 40.3 542.7 3’914’921 287.0

Data and metadata structure

Sensor data corpus

The sensor data corpus comprises two processing stages:

raw data streams stored in ROS bagfiles (aka logfiles),

camera and sonar images (aka datafiles) extracted from the logfiles.

The files are stored in a file tree hierarchy which groups them by session, dataset, and modality:

${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}

A typical logfile path has this form:

2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag

A typical datafile path has this form:

2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg

All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file entities.json.

Metadatabase

The metadatabase is provided in two equivalent forms:

as a standalone SQLite (https://www.sqlite.org/index.html) database file metadata.sqlite for users familiar with SQLite,

as a collection of CSV files in the csv/ directory for users who prefer other tools.

The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information.

An entity relationship diagram of the metadatabase tables is stored in the file entity_relationship_diagram.png. Each entity, its attributes, and relations are documented in detail in the file entities.json

Some general design remarks:

For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.

In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities stream and logfile are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.

A modality is not an attribute of a sensor alone, but of a datafile: Because a sensor is an attribute of a stream, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.

As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query:

SELECT PRINTF( '%s - %s', SUBSTR(session_start, 1, 10), SUBSTR(session_end, 1, 10)) AS 'Session dates', location_name_english AS Location, number_of_datasets AS 'Number of datasets', total_duration_of_datasets_h AS 'Total duration of datasets [h]', total_logfile_size_gb AS 'Total logfile size [GB]', number_of_images AS 'Number of images', total_image_size_gb AS 'Total image size [GB]' FROM location JOIN session USING (location_id) JOIN ( SELECT session_id, COUNT(dataset_id) AS number_of_datasets, ROUND( SUM(dataset_duration) / 3600, 1) AS total_duration_of_datasets_h, ROUND( SUM(total_logfile_size) / 10e9, 1) AS total_logfile_size_gb FROM location JOIN session USING (location_id) JOIN dataset USING (session_id) JOIN view_dataset_total_logfile_size USING (dataset_id) GROUP BY session_id ) USING (session_id) JOIN ( SELECT session_id, COUNT(datafile_id) AS number_of_images, ROUND(SUM(datafile_size) / 10e9, 1) AS total_image_size_gb FROM session JOIN dataset USING (session_id) JOIN stream USING (dataset_id) JOIN datafile USING (stream_id) GROUP BY session_id ) USING (session_id) ORDER BY session_id;

MetadataGlance: MetadataGlance provides PLSS data steward content for individual PLSS units.This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.

The global enterprise metadata management market is expected to grow at a 14.8% CAGR during the forecast period. In 2023, the market is currently valued at US$ 2,626.9 million. The enterprise metadata management market is expected to reach US$ 10,474.3 million by 2033. Future Market Insights specialists have observed a historical CAGR of 12.7% from 2018 to 2022.

Report Scope

This project will deliver an open source framework for metadata exploration, automatic text mining and information retrieval of polar data that uses the Apache Tika technology. Apache Tika is currently the de facto "babel fish", aiding in the automatic MIME detection, text extraction, and metadata classification of over 1200 data formats. The PI will expand Tika to handle polar data and scientific data formats, making Polar data more easily available, searchable, and retrievable by all major content management systems. The proposed activity will lay the framework for a thorough automatically generated inventory of polar metadata and data. Expanding Tika to handle polar data will also naturally invite the technology/open source community to deal with polar use cases, helping to increase understanding of the arctic. The resultant software produced through effort will be disseminated to the software and polar communities through the Apache Software Foundation. A computer science graduate student and postdoc will be exposed to Cryosphere and Arctic data, helping to train the next generation of cross disciplinary data scientists in the domain. The PI's Search Engines (20-40 students annual enrollment) and Software Architecture (30-50 students annual enrollment) graduate courses at USC will benefit from the Arctic cyberinfrastructure use cases disseminated through course projects and lecture material. The PI will also work collaboratively with NSF-funded projects dealing with projects focusing on the archiving, discovery and access of polar data, such as ACADIS and the Antarctic Master Directory.

Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""

This data release contains six different datasets that were used in the report SIR 2018-5108. These datasets contain discharge data, discrete dissolved-solids data, quality-control discrete dissolved data, and computed mean dissolved solids data that were collected at various locations between the Hoover Dam and the Imperial Dam. Study Sites: Site 1: Colorado River below Hoover Dam Site 2: Bill Williams River near Parker Site 3: Colorado River below Parker Dam Site 4: CRIR Main Canal Site 5: Palo Verde Canal Site 6: Colorado River at Palo Verde Dam Site 7: CRIR Lower Main Drain Site 8: CRIR Upper Levee Drain Site 9: PVID Outfall Drain Site 10: Colorado River above Imperial Dam Discrete Dissolved-solids Dataset and Replicate Samples for Discrete Dissolved-solids Dataset: The Bureau of Reclamation collected discrete water-quality samples for the parameter of dissolved-solids (sum of constituents). Dissolved-solids, measured in milligrams per liter, are the sum of the following constituents: bicarbonate, calcium, carbonate, chloride, fluoride, magnesium, nitrate, potassium, silicon dioxide, sodium, and sulfate. These samples were collected on a monthly to bimonthly basis at various time periods between 1990 and 2016 at Sites 1-5 and Sites 7-10. No data were collected for Site 6: Colorado River at Palo Verde Dam. The Bureau of Reclamation and the USGS collected discrete quality-control replicate samples for the parameter of dissolved-solids, sum of constituents measured in milligrams per liter. The USGS collected discrete quality-control replicate samples in 2002 and 2003 and the Bureau of Reclamation collected discrete quality-control replicate samples in 2016 and 2017. Listed below are the sites where these samples were collected at and which agency collected the samples. Site 3: Colorado River below Parker Dam: USGS and Reclamation Site 4: CRIR Main Canal: Reclamation Site 5: Palo Verde Canal: Reclamation Site 7: CRIR Lower Main Drain: Reclamation Site 8: CRIR Upper Levee Drain: Reclamation Site 9: PVID Outfall Drain: Reclamation Site 10: Colorado River above Imperial Dam: USGS and Reclamation Monthly Mean Datasets and Mean Monthly Datasets: Monthly mean discharge data (cfs), flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1990 to 2016 were computed using raw data from the USGS and the Bureau of Reclamation. This data were computed for all 10 sites. Flow weighted monthly mean dissolved-solids concentration and monthly mean dissolved-solids load were not computed for Site 2: Bill Williams River near Parker. The monthly mean datasets that were calculated for each month for the period between 1990 and 2016 were used to compute the mean monthly discharge and the mean monthly dissolved-solids load for each of the 12 months within a year. Each monthly mean was weighted by how many days were in the month and then averaged for each of the twelve months. This was computed for all 10 sites except mean monthly dissolved-solids load were not computed at Site 2: Bill Williams River near Parker. Site 8a: Colorado River between Parker and Palo Verde Valleys was computed by summing the data from sites 6, 7 and 8. Bill Williams Daily Mean Discharge, Instantaneous Dissolved-solids Concentration, and Daily Means Dissolved-solids Load Dataset: Daily mean discharge (cfs), instantaneous solids concentration (mg/L), and daily mean dissolved solids load were calculated using raw data collected by the USGS and the Bureau of Reclamation. This data were calculated for Site 2: Bill Williams River near Parker for the period of January 1990 to February 2016. Palo Verde Irrigation District Outfall Drain Mean Daily Discharge Dataset: The Bureau of Reclamation collected mean daily discharge data for the period of 01/01/2005 to 09/30/2016 at the Palo Verde Irrigation District (PVID) outfall drain using a stage-discharge relationship.

The NIST Extensible Resource Data Model (NERDm) is a set of schemas for encoding in JSON format metadata that describe digital resources. The variety of digital resources it can describe includes not only digital data sets and collections, but also software, digital services, web sites and portals, and digital twins. It was created to serve as the internal metadata format used by the NIST Public Data Repository and Science Portal to drive rich presentations on the web and to enable discovery; however, it was also designed to enable programmatic access to resources and their metadata by external users. Interoperability was also a key design aim: the schemas are defined using the JSON Schema standard, metadata are encoded as JSON-LD, and their semantics are tied to community ontologies, with an emphasis on DCAT and the US federal Project Open Data (POD) models. Finally, extensibility is also central to its design: the schemas are composed of a central core schema and various extension schemas. New extensions to support richer metadata concepts can be added over time without breaking existing applications. Validation is central to NERDm's extensibility model. Consuming applications should be able to choose which metadata extensions they care to support and ignore terms and extensions they don't support. Furthermore, they should not fail when a NERDm document leverages extensions they don't recognize, even when on-the-fly validation is required. To support this flexibility, the NERDm framework allows documents to declare what extensions are being used and where. We have developed an optional extension to the standard JSON Schema validation (see ejsonschema below) to support flexible validation: while a standard JSON Schema validater can validate a NERDm document against the NERDm core schema, our extension will validate a NERDm document against any recognized extensions and ignore those that are not recognized. The NERDm data model is based around the concept of resource, semantically equivalent to a schema.org Resource, and as in schema.org, there can be different types of resources, such as data sets and software. A NERDm document indicates what types the resource qualifies as via the JSON-LD "@type" property. All NERDm Resources are described by metadata terms from the core NERDm schema; however, different resource types can be described by additional metadata properties (often drawing on particular NERDm extension schemas). A Resource contains Components of various types (including DCAT-defined Distributions) that are considered part of the Resource; specifically, these can include downloadable data files, hierachical data collecitons, links to web sites (like software repositories), software tools, or other NERDm Resources. Through the NERDm extension system, domain-specific metadata can be included at either the resource or component level. The direct semantic and syntactic connections to the DCAT, POD, and schema.org schemas is intended to ensure unambiguous conversion of NERDm documents into those schemas. As of this writing, the Core NERDm schema and its framework stands at version 0.7 and is compatible with the "draft-04" version of JSON Schema. Version 1.0 is projected to be released in 2025. In that release, the NERDm schemas will be updated to the "draft2020" version of JSON Schema. Other improvements will include stronger support for RDF and the Linked Data Platform through its support of JSON-LD.