This document summarizes a webinar on metadata for managing scientific research data. The webinar covered why metadata is important for scientific data management, definitions of data and metadata, selected metadata standards including Dublin Core, Darwin Core and FGDC, challenges in generating metadata and opportunities to address these challenges, and advice for getting started with metadata. The webinar emphasized that metadata standards provide guidelines not strict rules, and encouraged participants to keep metadata simple while aiming to facilitate reuse of data.
About the Webinar
In May 2012, the Library of Congress announced a new modeling initiative focused on reflecting the MARC 21 library standard as a Linked Data model for the Web, with an initial model to be proposed by the consulting company Zepheira. The goal of the initiative is to translate the MARC 21 format to a Linked Data model while retaining the richness and benefits of existing data in the historical format.
In this webinar, Eric Miller of Zepheira will report on progress towards this important goal, starting with an analysis of the translation problem and concluding with potential migration scenarios for a broad-based transition from MARC to a new bibliographic framework.
Slides from my Metadata Workshop at Content Strategy Applied 2012. The session included several hands on exercises, which is where a lot of the interesting conversation took place.
Libraries around the world have a long tradition of maintaining authority files to assure the consistent presentation and indexing of names. As library authority files have become available online, the authority data has become accessible -- and many have been published as Linked Open Data (LOD) -- but names in one library authority file typically had no link to corresponding records for persons and organizations in other library authority files. After a successful experiment in matching the Library of Congress/NACO authority file with the German National Library's authority file, an online system called the Virtual International Authority File was developed to facilitate sharing by ingesting, matching, and displaying the relations between records in multiple authority files.
The Virtual International Authority File (VIAF) has grown from three source files in 2007 to more than two dozen files today. The system harvests authority records, enhances them with bibliographic information and brings them together into clusters when it is confident the records describe the same identity. Although the most visible part of VIAF is a HTML interface, the API beneath it supports a linked data view of VIAF with URIs representing the identities themselves, not just URIs for the clusters. It supports names for person, corporations, geographic entities, works, and expressions. With English, French, German, Spanish interfaces (and a Japanese in process), the system is used around the world, with over a million queries per day.
Speaker
Thomas Hickey is Chief Scientist at OCLC where he helped found OCLC Research. Current interests include metadata creation and editing systems, authority control, parallel systems for bibliographic processing, and information retrieval and display. In addition to implementing VIAF, his group looks into exploring Web access to metadata, identification of FRBR works and expressions in WorldCat, the algorithmic creation of authorities, and the characterization of collections. He has an undergraduate degree in Physics and a Ph.D. in Library and Information Science.
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
About the Webinar
In May 2012, the Library of Congress announced a new modeling initiative focused on reflecting the MARC 21 library standard as a Linked Data model for the Web, with an initial model to be proposed by the consulting company Zepheira. The goal of the initiative is to translate the MARC 21 format to a Linked Data model while retaining the richness and benefits of existing data in the historical format.
In this webinar, Eric Miller of Zepheira will report on progress towards this important goal, starting with an analysis of the translation problem and concluding with potential migration scenarios for a broad-based transition from MARC to a new bibliographic framework.
Slides from my Metadata Workshop at Content Strategy Applied 2012. The session included several hands on exercises, which is where a lot of the interesting conversation took place.
Libraries around the world have a long tradition of maintaining authority files to assure the consistent presentation and indexing of names. As library authority files have become available online, the authority data has become accessible -- and many have been published as Linked Open Data (LOD) -- but names in one library authority file typically had no link to corresponding records for persons and organizations in other library authority files. After a successful experiment in matching the Library of Congress/NACO authority file with the German National Library's authority file, an online system called the Virtual International Authority File was developed to facilitate sharing by ingesting, matching, and displaying the relations between records in multiple authority files.
The Virtual International Authority File (VIAF) has grown from three source files in 2007 to more than two dozen files today. The system harvests authority records, enhances them with bibliographic information and brings them together into clusters when it is confident the records describe the same identity. Although the most visible part of VIAF is a HTML interface, the API beneath it supports a linked data view of VIAF with URIs representing the identities themselves, not just URIs for the clusters. It supports names for person, corporations, geographic entities, works, and expressions. With English, French, German, Spanish interfaces (and a Japanese in process), the system is used around the world, with over a million queries per day.
Speaker
Thomas Hickey is Chief Scientist at OCLC where he helped found OCLC Research. Current interests include metadata creation and editing systems, authority control, parallel systems for bibliographic processing, and information retrieval and display. In addition to implementing VIAF, his group looks into exploring Web access to metadata, identification of FRBR works and expressions in WorldCat, the algorithmic creation of authorities, and the characterization of collections. He has an undergraduate degree in Physics and a Ph.D. in Library and Information Science.
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
The Dublin Core 1:1 Principle in the Age of Linked DataRichard Urban
Presentation given at the International Conference on Dublin Core and Metadata Applications, Austin, TX. October 9, 2014. See associated paper http://dcevents.dublincore.org/IntConf/dc-2014/paper/view/263
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
This presentation focuses on providing means for exploring Linked Data. In particular, it gives an overview of current visualization tools and techniques, looking at semantic browsers and applications for presenting the data to the end used. We also describe existing search options, including faceted search, concept-based search and hybrid search, based on a mix of using semantic information and text processing. Finally, we conclude with approaches for Linked Data analysis, describing how available data can be synthesized and processed in order to draw conclusions.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
SDA2013 Pundit: Creating, Exploring and Consuming AnnotationsMarco Grassi
This paper presents Pundit, a novel semantic web annotation tool, and demonstrates its use in producing structured data out of users annotations. Pundit allows communities of scholars to produce machine-readable annotations that can be made public and thus consumable as web data via SPARQL and ad-hoc REST APIs.
Pundit is highly configurable and can deployed in custom instances to include well-defined and agreed annotation vocabularies. Such instances can be distributed as bookmaklets to community users so they can create uniformly structured data in a certain application scenario. Basing on the provided APIs, some demonstrative applications have been developed, exploring different use scenarios, ranging from philosophy to journalism and cultural heritage.
The main aim of this paper is to demonstrate how such uniformly structured annotations can be quickly re-used on the web to make information discoverable or to visualize it in interesting ways.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
Mending the Gap between Library's Electronic and Print Collections in ILS and...New York University
This presentation proposed a conceptual model to model user's info seeking behavior in the context of their experience and use the model to improve library's collections and services using St. John's University Libraries for case study. It reviewed Web content technologies offered by IT vendors, and compared what offered in content technologies by Library IT vendors. To fill in the gap, It developed the preliminary proposal for 1) required data architecture in SOA framework, 2) desired features for managing library print and electronic content on library's website, 3) adoption of Semantic Web standards and technologies for managing library resources, and 4) the case study scenario with sample conceptual model.
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
Towards digitizing scholarly communicationSören Auer
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
The Dublin Core 1:1 Principle in the Age of Linked DataRichard Urban
Presentation given at the International Conference on Dublin Core and Metadata Applications, Austin, TX. October 9, 2014. See associated paper http://dcevents.dublincore.org/IntConf/dc-2014/paper/view/263
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
This presentation focuses on providing means for exploring Linked Data. In particular, it gives an overview of current visualization tools and techniques, looking at semantic browsers and applications for presenting the data to the end used. We also describe existing search options, including faceted search, concept-based search and hybrid search, based on a mix of using semantic information and text processing. Finally, we conclude with approaches for Linked Data analysis, describing how available data can be synthesized and processed in order to draw conclusions.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
SDA2013 Pundit: Creating, Exploring and Consuming AnnotationsMarco Grassi
This paper presents Pundit, a novel semantic web annotation tool, and demonstrates its use in producing structured data out of users annotations. Pundit allows communities of scholars to produce machine-readable annotations that can be made public and thus consumable as web data via SPARQL and ad-hoc REST APIs.
Pundit is highly configurable and can deployed in custom instances to include well-defined and agreed annotation vocabularies. Such instances can be distributed as bookmaklets to community users so they can create uniformly structured data in a certain application scenario. Basing on the provided APIs, some demonstrative applications have been developed, exploring different use scenarios, ranging from philosophy to journalism and cultural heritage.
The main aim of this paper is to demonstrate how such uniformly structured annotations can be quickly re-used on the web to make information discoverable or to visualize it in interesting ways.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
Mending the Gap between Library's Electronic and Print Collections in ILS and...New York University
This presentation proposed a conceptual model to model user's info seeking behavior in the context of their experience and use the model to improve library's collections and services using St. John's University Libraries for case study. It reviewed Web content technologies offered by IT vendors, and compared what offered in content technologies by Library IT vendors. To fill in the gap, It developed the preliminary proposal for 1) required data architecture in SOA framework, 2) desired features for managing library print and electronic content on library's website, 3) adoption of Semantic Web standards and technologies for managing library resources, and 4) the case study scenario with sample conceptual model.
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
Towards digitizing scholarly communicationSören Auer
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
RDAP 15: Beyond Metadata: Leveraging the “README” to support disciplinary Doc...ASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Part of “Beyond metadata: Supporting non-standardized documentation to facilitate data reuse”
LAC Group - Metadata for mere mortals (Choosing standards)LAC Group
Metadata for mere mortals - Part 2: Choosing standards
Presented by Erin Antognoli, Metadata Librarian
Welcome to part 2 of our Metadata for mere mortals series, which serves as a basic introduction to the principles and function of metadata for content and digital asset managers who lack formal training in this area.
There are a lot of metadata standards out there, and in this video, we will examine:
- What questions to ask to ensure you will meet the needs of your community/user group.
- General subject or subject specific metadata standards like Dublin Core and ISO 19115, respectively.
- The pros and cons of such metadata standards.
Check out Part 1 of this webinar series: https://lac.gp/MetadataIntro
Download our free metadata report, Making sense of metadata: https://lac.gp/MetadataReport
Contact us: https://lac-group.com/contact-us/
Scientific discovery and innovation in an era of data-intensive science
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The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies.
Today, we count more than 10,000 datasets made available online following Semantic Web standards.
A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things.
The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before.
First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines.
In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark.
Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage.
However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available.
Thus, we introduce a distributed approach of quality assessment of large RDF datasets.
It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data.
Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information.
As a result, it has become difficult to efficiently process these large RDF datasets.
Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size.
Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code.
We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches.
More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach.
The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets.
All the proposed approaches during this thesis are integrated into the larger SANSA framework.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the closing segment of the NISO training series "AI & Prompt Design." Session Eight: Limitations and Potential Solutions, was held on May 23, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the seventh segment of the NISO training series "AI & Prompt Design." Session 7: Open Source Language Models, was held on May 16, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the sixth segment of the NISO training series "AI & Prompt Design." Session Six: Text Classification with LLMs, was held on May 9, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the fifth segment of the NISO training series "AI & Prompt Design." Session Five: Named Entity Recognition with LLMs, was held on May 2, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the fourth segment of the NISO training series "AI & Prompt Design." Session Four: Structured Data and Assistants, was held on April 25, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the third segment of the NISO training series "AI & Prompt Design." Session Three: Beginning Conversations, was held on April 18, 2024.
This presentation was provided by Kaveh Bazargan of River Valley Technologies, during the NISO webinar "Sustainability in Publishing." The event was held April 17, 2024.
This presentation was provided by Dana Compton of the American Society of Civil Engineers (ASCE), during the NISO webinar "Sustainability in Publishing." The event was held April 17, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, during the second segment of the NISO training series "AI & Prompt Design." Session Two: Large Language Models, was held on April 11, 2024.
This presentation was provided by Teresa Hazen of the University of Arizona, Geoff Morse of Northwestern University. and Ken Varnum of the University of Michigan, during the Spring ODI Conformance Statement Workshop for Libraries. This event was held on April 9, 2024
This presentation was provided by William Mattingly of the Smithsonian Institution, during the opening segment of the NISO training series "AI & Prompt Design." Session One: Introduction to Machine Learning, was held on April 4, 2024.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the eight and final session of NISO's 2023 Training Series on Text and Data Mining. Session eight, "Building Data Driven Applications" was held on Thursday, December 7, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the seventh session of NISO's 2023 Training Series on Text and Data Mining. Session seven, "Vector Databases and Semantic Searching" was held on Thursday, November 30, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the sixth session of NISO's 2023 Training Series on Text and Data Mining. Session six, "Text Mining Techniques" was held on Thursday, November 16, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the fifth session of NISO's 2023 Training Series on Text and Data Mining. Session five, "Text Processing for Library Data" was held on Thursday, November 9, 2023.
This presentation was provided by Todd Carpenter, Executive Director, during the NISO webinar on "Strategic Planning." The event was held virtually on November 8, 2023.
This presentation was provided by Rhonda Ross of CAS, a division of the American Chemical Society, and Jonathan Clark of the International DOI Foundation, during the NISO webinar on "Strategic Planning." The event was held virtually on November 8, 2023.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the fourth session of NISO's 2023 Training Series on Text and Data Mining. Session four, "Data Mining Techniques" was held on Thursday, November 2, 2023.
This presentation was provided by Tiffany Straza of UNESCO, during the two-day "NISO Tech Summit: Reflections Upon The Year of Open Science." Day two was held on October 26, 2023.
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http://sandymillin.wordpress.com/iateflwebinar2024
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NISO/DCMI Webinar: Metadata for Managing Scientific Research Data
1. Metadata for Managing
Scientific Research Data
NISO/DCMI Webinar:
August 22, 2012
Jane Greenberg, Professor and Director of
the SILS Metadata Research Center
janeg@email.unc.edu
2. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
3. Why should we care?
BIG stuff
▪ Digital data deluge (Hey & Trefethen, 2003)
▪ Big data (New York Times)
2008
▪ The fourth paradigm (Jim Gray, 2007)
Just as important
▪ The long tail (Heidorn, 2008)
▪ CODATA/Data-at-Risk Task Group
▪ Scholarly communications, data citation
Technological affordances for improving and
advancing science
4. Cultural shift toward data sharing
▪ National and international policies
– US NSF and NIH [1, 2]
– OECD (Organisation for Economic Co-operation and
Development) [3]
– INSPIRE Infrastructure for Spatial Information in the European
Community EU Commission [4]
– UK Medical Research Council [5]
Dryad ―enables scientists to validate
published findings, explore new analysis
methodologies, repurpose data for research
questions unanticipated by the original
authors, and perform synthetic studies.‖
(http://datadryad.org/)
5. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
6. Data
▪ No single agreed upon definition
▪ One person‘s data is another person‘s
information
▪ Data often implies the ―raw‖ stuff lacking
context
– Scholarly context, written assessment
▪ ―Essence of science‖ (Greenberg, et al, 2009)
▪ What is science?
– The Archaeology Data Service (ADS)
archaeologydataservice.ac.uk
7. Data quantity type The Dryad
Repository
3162 Plain Text
I know it when I see it 476 Microsoft Excel
308 Adobe Portable Document
Format
By example: Traditional 302 Comma-separated values
observations, numbers, and 252 Nexus
measures stored in spreadsheets 153 Microsoft Excel OpenXML
and databases, fossils, 108 Microsoft Word
phylogenetic trees, and herbarium 80 Zip file
samples (White, 2008) 62 JPEG image
45 Microsoft Word OpenXML
Other disciplines 40 Extensible Markup Language
▪ Bioinformatics: Gene 35 Hypertext Markup Language
expressions, DNA transcription 21 Rich Text Format
to RNA translation 16 FASTA sequence file
15 Tag Image File Format
▪ Geology, agriculture,
14 Postscript Files
surveillance, and historical
2 Video Quicktime
manuscript research:
2 Mathematica Notebook
Hyperspectral remote sensing
1 Microsoft Powerpoint
(email w/R. Scherle, July 2012)
8. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
9. Metadata defined
……data about data
…….information about data
▪―Metadata or ‗data about data‘ describes the
content, quality, condition, and other
characteristics of data.‖ (FGDC Metadata WG,
1998)
▪Structured information about an object (data)
that facilitates functions associated with the
object. (Greenberg, 2002, 2003, 2009)
10. Typical functions
Control
Discover Manage
rights
Identify Certify Indicate
versions authenticity status
Mark conent Situate Describe
strucure geospatially processes
11. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
13. Metadata for Scientific Research Data
Descriptive
– General to granular
▪Value (addressing a topic, ―aboutness‖)
– Topical (ontologies, subject heading lists/thesauri,
taxonomies)
▪Named entities
– Name authority files (people, organizations,
geographical jurisdictions, structures, and events)
▪Geo-spatial (coordinates)
▪Temporal data (ISO 8601/ W3CDTF, or …)
14. Given the messiness…
―I cannot tell you exactly what metadata
standards, vocabularies, etc. to use…‖
15. Examining metadata schemes
Objectives and Domains Architectural layout
principles
• Objectives • Discipline • Structural design
• Genre • Extent
• Principles
• Format • Granularity
Metadata Objectives and principles, Domain, and
Architectural Layout (MODAL) framework
(Greenberg, 2005; Willis, et al, JASIST 2012)
16. Objectives and Domains Architectural
Simple principles layout
schemes
[6] • Interoperability • Multi- • Primarily flat
• Easy to disciplinary • Minimal with
generate, • Any genre or means to
lower barrier format extend
to produce • General (not
granular)
Dublin Core
Metadata
Element Set
(DCMES)
ver.1.1
US MARC • Need training • Primarily flat
bibliographic • Extensible
format
DataCite • Primarily flat
18. DataCite example, ver.2.2 [8]
National Institute for
Environmental Studies and
Center for Climate System
Research Japan
19. US MARC bibliographic
format: World Ocean
Circulation Experiment global
data (Moss Landing Marine
Labs and the Monterey Bay
Aquarium Research Institute
Library) [9]
20. Objectives and Domains Architectural
Simple/ principles layout
moderate Interoperability Greater domain Primarily flat
balanced focus Extensibility—
schemes w/specific Genera via connecting
needs diversity within Slightly more
Generation a domain granular
requires more
expertise
Darwin Core
Access to • Not as flat
Biological
Collections Data
(ABCD)
Ecological
Metadata
Language
DCMI Terms • Graph approach
21. Wieczorek, et al. (2012). Darwin Core: An Evolving Community-
Developed Biodiversity Data Standard.
PLoS One. 2012; 7(1): e29715: doi: 10.1371/journal.pone.0029715.
23. abstract educationLevel modified
accessRights extent provenance
accrualMethod format publisher
accrualPeriodicity hasFormat references
accrualPolicy hasPart relation
alternative hasVersion replaces
audience identifier requires
available instructionalMethod rights
bibliographicCitation isFormatOf rightsHolder
conformsTo isPartOf source
contributor isReferencedBy spatial
coverage isReplacedBy subject
created isRequiredBy tableOfContents
creator issued temporal
date isVersionOf title
dateAccepted language type
dateCopyrighted license valid
dateSubmitted mediator Properties in the /terms/
description medium namespace
24. Objectives and Domains Architectural
Complex principles layout
schemes
Interoperability • Genre focus Hierarchical
level • Format Extensive
Generation variation Granular
requires greater
expertise
FGDC
DDI
Content Standard for Digital Data Document Initiative (DDI)
Geospatial Metadata
(CSDGM)/FGDC
1. Identification Information (M) 1. Concept
2. Data Quality Information 2. Collecting
3. Spatial Data Organization Information 3. Processing Archiving
4. Spatial Reference Information 4. Distribution Archiving
5. Entity and Attribute Information 5. Discovery
6. Distribution Information 6. Analysis
7. Metadata Reference Information (M) 7. Repurposing
25. Summary for descriptive schemes
▪ Simple: Interoperable, Easy to generate/low barrier,
generally multidisciplinary, genera/format agnostics,
primarily flat, general (not granular), 15-25 properties
▪ Simple/moderate: Interoperability balanced
w/specific needs, generation requires more expertise,
greater domain focus, extensible--via connecting to
other schemes, more granular, more properties
▪ Complex: Interoperable level, generation requires
expertise, genera focus/format variation, hierarchical,
granular, and extensive (100+ properties)
26.
27. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
28. Challenges and opportunities
Challenges Opportunities
Workflow/When to Educate scientists early (Qin, 2009)
▪ Stop
generate the here Integrate into social setting w/Center for
metadata? Embedded Networked Sensing
(CENS) (Borgman, Mayernik, etc., 2009-current;
Mayernik‘s dissertation, 2011)
Methods for generating Use automatic techniques as much as possible,
metadata (labor leverage human expertise (Dryad, DataOne Excel
intensive) project)
Too many standards Don‘t panic, join communities, look for
Which one do I use? examples. (If you can‘t find them?)
Do I need to No. Explore and develop a best practice.
implement my Pursue a 2 pronged approach (Greenberg, et al,
metadata as linked 2009)
data.
29. Jumping in…
1. DCMI/NISO Seminars !!
2. DCMI Science and Metadata Community
(http://wiki.dublincore.org/index.php/DCMI_Science_And_Metadata)
3. Digital Curation Center (DCC)
(http://www.dcc.ac.uk/)
4. The Research Data Management
Training, or MANTRA project
(http://datalib.edina.ac.uk/mantra/)
5. DataONE workshops and tutorials
(www.dataone.org/)
30. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
31. Concluding comments
▪ Standards are guidelines; no police
– Aim for reasonable quality
▪ KISS: Keep it simple stupid
– What’s vital; what will aid reuse?
▪ Help to move the practice forward
– Share what you learn
▪ Nothing new/it‘s all new
– Data documentation since ancient times
– SILOS; let‘s break them down (Willis, et al, 2012)
– Greater connectivity than ever
– Cross-disciplinary approaches for problem solving
32. Overview
▪ Why should we care?
▪ What is data?
▪ What is metadata‘s role w.r.t data?
▪ Selected metadata standards
▪ Challenges, opportunities, and jumping in
▪ Concluding comments
▪ Q&A
33. Footnotes
[1] NSF Data Sharing Policy: http://www.nsf.gov/bfa/dias/policy/dmp.jsp.
[2] NIH Data Sharing Policy: http://grants.nih.gov/grants/policy/data_sharing/.
[3] ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT/Data and
Metadata Reporting and Presentation Handbook: http://www.oecd.org/std/37671574.pdf.
[4] The INSPIRE Infrastructure for Spatial Information in the European Community):
http://inspire.ec.europa.eu/index.cfm/pageid/48. directive released 15 May 2007 and will be
implemented in various stages, with full implementation required by 2019, and aims to create a
European Union (EU) spatial data infrastructure.
[5] UK medical research council:
http://www.mrc.ac.uk/Ourresearch/Ethicsresearchguidance/datasharing/index.html.
[6] The DCMI Glossary (scroll down for ―schema‖ entry):
http://dublincore.org/documents/usageguide/glossary.shtml#schema.
[7] Dublin Core Example: Data from: Divergence time estimation using fossils as terminal taxa
and the origins of Lissamphibia (Dryad repository):
http://datadryad.org/resource/doi:10.5061/dryad.8120?show=full.
[8] National Institute for Environmental Studies and Center for Climate System Research
Japan—animation data (DataCite): http://schema.datacite.org/meta/kernel-
2.2/example/datacite-metadata-sample-v2.2.xml.
[9] US MARC bibliographic format: World Ocean Circulation Experiment global data (Moss
Landing Marine Labs and the Monterey Bay Aquarium Research Institute Library):
http://mlml.kohalibrary.com/cgi-bin/koha/opac-detail.pl?biblionumber=9282.