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TwitterThe 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.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Information on samples submitted for RNAseq
Rows are individual samples
Columns are: ID Sample Name Date sampled Species Sex Tissue Geographic location Date extracted Extracted by Nanodrop Conc. (ng/µl) 260/280 260/230 RIN Plate ID Position Index name Index Seq Qubit BR kit Conc. (ng/ul) BioAnalyzer Conc. (ng/ul) BioAnalyzer bp (region 200-1200) Submission reference Date submitted Conc. (nM) Volume provided PE/SE Number of reads Read length
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Unique values and counts of metadata facet fields.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Metadata for the 16,012 microbial samples included in this database. Metadata was collated from the originally published studies, available supplementary information, and from online databases
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TwitterMetadata are contextual data about your experimental data. Metadata are the who, what, when, where, and why of these data. Metadata puts these data into context. In microbiome research, metadata includes information about the sample: when it was collected, where it was collected from, what kind of sample it is, and what were the properties of the environment or experimental condition from which the sample was taken. Information about sample processing is also metadata: methods used to extract and purify molecules (e.g., DNA) from the sample, type of DNA sequencing or other ’omics analyses done, and where the raw experimental data are located. This documentation includes information on the following topics: What are metadata? What kinds of metadata are there? Why are metadata important? Why should metadata be standardized? Why should metadata be machine readable? What is an ontology? Why are ontologies important? What are some examples of ontologies? The documentation also includes further resources on metadata guides and tools and tips for formatting metadata for maximum utility.
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Time-based metadata formatted for TimelineJS or other applications.
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Twitterhttps://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy
The Metadata Management Tools market is booming, projected to reach $40 billion by 2033, driven by cloud adoption, data security needs, and the rise of data analytics. Learn about key trends, leading players (Oracle, SAP, IBM), and regional market shares in this comprehensive market analysis.
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Twitterincluded in this dataset are helpful links and files on the metadata schema for the CNRA Open Data Platform
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TwitterStores physical and logical information about relational databases and record structures to assist in data identification and management.
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| BASE YEAR | 2024 |
| HISTORICAL DATA | 2019 - 2023 |
| REGIONS COVERED | North America, Europe, APAC, South America, MEA |
| REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
| MARKET SIZE 2024 | 2.69(USD Billion) |
| MARKET SIZE 2025 | 2.92(USD Billion) |
| MARKET SIZE 2035 | 6.5(USD Billion) |
| SEGMENTS COVERED | Application, Deployment Type, End User, Functionality, Regional |
| COUNTRIES COVERED | US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA |
| KEY MARKET DYNAMICS | Data governance compliance, Increased data complexity, Growing cloud adoption, Enhanced data analytics, Demand for real-time insights |
| MARKET FORECAST UNITS | USD Billion |
| KEY COMPANIES PROFILED | Informatica, IBM, Hitachi Vantara, Snowflake, AWS, Oracle, Alation, Collibra, DataOps, SAP, Microsoft, SAS, Denodo, Ataccama, Talend |
| MARKET FORECAST PERIOD | 2025 - 2035 |
| KEY MARKET OPPORTUNITIES | Growing demand for data governance, Increasing cloud adoption, Integration with AI analytics, Rising regulatory compliance needs, Enhanced data lineage capabilities |
| COMPOUND ANNUAL GROWTH RATE (CAGR) | 8.4% (2025 - 2035) |
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TwitterMexico Marine Research Metadata DatabaseThis project compiled metadata on available datasets produced by marine research in Mexico. The data is categorized by region, theme, species (when applicable), and research fields. This dataset corresponds to the associated peer-reviewed paper, the living database can be accessed at http://infoceanos.conabio.gob.mx.Mexico Metadata Database DataDryad.csv
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TwitterThis data dictionary describes relevant fields from secondary data sources that can assist with modeling the conditions of use for a chemical when performing a chemical assessment. Information on how to access the secondary data sources are included. This dataset is associated with the following publication: Chea, J.D., D.E. Meyer, R.L. Smith, S. Takkellapati, and G.J. Ruiz-Mercado. Exploring automated tracking of chemicals through their conditions of use to support life cycle chemical assessment. JOURNAL OF INDUSTRIAL ECOLOGY. Berkeley Electronic Press, Berkeley, CA, USA, 29(2): 413-616, (2025).
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Twitterhttps://www.gnu.org/licenses/gpl-3.0.htmlhttps://www.gnu.org/licenses/gpl-3.0.html
Gms-index-mediator is a standalone index for spatio-temporal data acting as a mediator between an application and a database. Even modern databases need several minutes to execute a spatio-temporal query to huge tables containing several million entries. Our index-mediator speeds the execution of such queries up by several magnitues, resulting in response times around 100ms. This version is tailored towards the GeoMultiSens database, but can be adapted to work with custom table layouts with reasonable effort.
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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These research datasets are the updated version of the conference poster "Research data repositories and their metadata: A comparative study," presented by Ms. Kavya Asok and Ms. Snigdha Dandpat in a Conference on Open and FAIR Data Ecosystem: Principles, Policies, and Platforms scheduled from 11th -13th September 2023, at IIC, New Delhi. The study describes the features of a select number of RDRs and analyzes their metadata practices.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.
The materials included in the collection consist of the following:
esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.
statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.
The metadata fields consist of the following:
Fieldname
Description
abstract_data
A brief synopsis or abstract about the learning resource
abstract_format
Declaration for how the abstract description will be represented.
access_conditions
Conditions upon which the resource can be accessed beyond cost, e.g., login required.
access_cost
Yes or No choice stating whether othere is a fee for access to or use of the resource.
accessibililty_features_name
Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.
accessibililty_summary
A human-readable summary of specific accessibility features or deficiencies.
author_names
List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system
author_org
- name
- name_identifier
- name_identifier_type
- Name of organization authoring the learning resource.
- The unique identifier for the organization authoring the resource.
- The identifier scheme associated with the unique identifier for the organization authoring the resource.
authors - givenName - familyName - name_identifier - name_identifier_type
- Given or first name of person(s) authoring the resource.
- Last or family name of person(s) authoring the resource.
- The unique identifier for the person(s) authoring the resource.
- The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.
citation
Preferred Form of Citation.
completion_time
Intended Time to Complete
contact - name - org - email
- Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
- Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
- (excluded) Contact email address.
contributor_orgs
- name
- name_identifier
- name_identifier_type
- type
- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.
- The unique identifier for the organization contributing to the resource.
- The identifier scheme associated with the unique identifier for the organization contributing to the resource.
- Type of contribution to the resource made by an organization.
contributors
- familyName
- givenName
- name_identifier
- name_identifier_type
contributors.type
Type of contribution to the resource made by a person.
created
The date on which the metadata record was first saved as part of the input workflow.
creator
The name of the person creating the MD record for a resource.
credential_status
Declaration of whether a credential is offered for comopletion of the resource.
ed_frameworks - name - description - nodes.name
- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.
- A description of one or more subcategories of an educational framework to which a resource is associated.
- The name of a subcategory of an educational framework to which a resource is associated.
expertise_level
The skill level targeted for the topic being taught.
id
Unique identifier for the MD record generated by the system in UUID format.
keywords
Important phrases or words used to describe the resource.
language_primary
Original language in which the learning resource being described is published or made available.
languages_secondary
Additional languages in which the resource is tranlated or made available, if any.
license
A license for use of that applies to the resource, typically indicated by URL.
locator_data
The identifier for the learning resource used as part of a citation, if available.
locator_type
Designation of citation locatorr type, e.g., DOI, ARK, Handle.
lr_outcomes
Descriptions of what knowledge, skills or abilities students should learn from the resource.
lr_type
A characteristic that describes the predominant type or kind of learning resource.
media_type
Media type of resource.
modification_date
System generated date and time when MD record is modified.
notes
MD Record Input Notes
pub_status
Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.
published
Date of first broadcast / publication.
publisher
The organization credited with publishing or broadcasting the resource.
purpose
The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.
rating
The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.
ratings
Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.
resource_modification_date
Date in which the resource has last been modified from the original published or broadcast version.
status
System generated publication status of the resource w/in the registry as a yes for published or no for not published.
subject
Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.
submitter_email
(excluded) Email address of person who submitted the resource.
submitter_name
Submission Contact Person
target_audience
Audience(s) for which the resource is intended.
title
The name of the resource.
url
URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.
usage_info
Descriptive information about using the resource, not addressed by the License information field.
version
The specific version of the resource, if declared.
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TwitterThe data are qualitative data consisting of notes recorded during meetings, workshops, and other interactions with case study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data cannot be accessed by anyone outside of the research team because of the potential to identify human participants. Format: The data are qualitative data contained in Microsoft Word documents. This dataset is associated with the following publication: Eisenhauer, E., K. Maxwell, B. Kiessling, S. Henson, M. Matsler, R. Nee, M. Shacklette, M. Fry, and S. Julius. Inclusive engagement for equitable resilience: community case study insights. Environmental Research Communications. IOP Publishing, BRISTOL, UK, 6: 125012, (2024).
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This sample was drawn from the Crossref API on March 8, 2022. The sample was constructed purposefully on the hypothesis that records with at least one known issue would be more likely to yield issues related to cultural meanings and identity. Records known or suspected to have at least one quality issue were selected by the authors and Crossref staff. The Crossref API was then used to randomly select additional records from the same prefix. Records in the sample represent 51 DOI prefixes that were chosen without regard for the manuscript management or publishing platform used, as well as 17 prefixes for journals known to use the Open Journal Systems manuscript management and publishing platform. OJS was specifically identified due to the authors' familiarity with the platform, its international and multilingual reach, and previous work on its metadata quality.
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TwitterData (2011) associated to the publication "A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis. Agriculture, Ecosystems and Environment. Volume 144, Issue 1, November 2011, Pages 175-187"
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TwitterThe 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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Twitterhttps://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy
| BASE YEAR | 2024 |
| HISTORICAL DATA | 2019 - 2023 |
| REGIONS COVERED | North America, Europe, APAC, South America, MEA |
| REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
| MARKET SIZE 2024 | 2.48(USD Billion) |
| MARKET SIZE 2025 | 2.64(USD Billion) |
| MARKET SIZE 2035 | 5.0(USD Billion) |
| SEGMENTS COVERED | Deployment Type, Application Area, End User, Organization Size, Regional |
| COUNTRIES COVERED | US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA |
| KEY MARKET DYNAMICS | Data governance compliance needs, Increasing data volume, Growing need for data integration, Rising adoption of cloud services, Emphasis on data security |
| MARKET FORECAST UNITS | USD Billion |
| KEY COMPANIES PROFILED | Syncsort, Collibra, Informatica, SAP, Alation, Denodo, TIBCO Software, Microsoft, Talend, erwin, Adaptive, Ataccama, DataGalaxy, IBM, Oracle |
| MARKET FORECAST PERIOD | 2025 - 2035 |
| KEY MARKET OPPORTUNITIES | Data privacy regulations compliance, Integration with AI technologies, Cloud-based solutions growth, Increasing data volume management, Enhanced data governance needs |
| COMPOUND ANNUAL GROWTH RATE (CAGR) | 6.6% (2025 - 2035) |
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TwitterThe 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.