This document discusses metadata and data repositories. It defines metadata as data about data that provides important contextual information such as how the data was collected, who collected it, when it was collected, etc. This makes data findable, accessible, interoperable and reusable. Data repositories are services that store, manage and provide access to research materials while also ensuring long-term security, accessibility, integrity and discoverability of datasets. They allow datasets to be cited with identifiers like DOIs. Examples of data repositories discussed are the Environmental Data Initiative Repository and Knowledge Network for Biocomplexity.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
This is module 4 in the EDI Data Publishing training course. In this module, you will learn how to group your data files and other information products into a publishable unit.
The document discusses citing and linking data through various discovery services. It identifies the three main search engines for discovering data as EDI Data Search, DataONE Data Search, and Google Dataset Search. It provides instructions for creating a local data catalog on a website by linking data titles and URLs. Additionally, it promotes getting an ORCID identifier to link research profiles and notes the growing number of EDI services that help with data reuse, including ingestion scripts, APIs, notifications, and provenance tracking.
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
This is module 10 in the EDI Data Publishing training course. In this module, you will receive an introduction to what a data package is, how DOIs are assigned to data packages, and the repository's steps to insert a data package.
This is module 6 in the EDI Data Publishing training course. In this module, you will learn how to create quality metadata and be introduced to the landscape of data repositories and their functions.
This is module 4 in the EDI Data Publishing training course. In this module, you will learn how to group your data files and other information products into a publishable unit.
The document discusses citing and linking data through various discovery services. It identifies the three main search engines for discovering data as EDI Data Search, DataONE Data Search, and Google Dataset Search. It provides instructions for creating a local data catalog on a website by linking data titles and URLs. Additionally, it promotes getting an ORCID identifier to link research profiles and notes the growing number of EDI services that help with data reuse, including ingestion scripts, APIs, notifications, and provenance tracking.
This is module 5 in the EDI Data Publishing training course. In this module, you will learn how to properly format a data file for publishing in the EDI Repository.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Rots RDAP11 Data Archives in Federal AgenciesASIS&T
Arnold Rots, VAO; Data Archives in Federal Agencies; RDAP11 Summit
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
This is module 11 in the EDI Data Publishing training course. In this module, you will learn the procedure to upload a data package to the EDI Repository.
Comeaux RDAP11 Data Archives in Federal AgenciesASIS&T
Joey Comeaux, CICL RDA; Data Archives in Federal Agencies
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Smith RDAP11 NSF Data Management Plan Case StudiesASIS&T
MacKenzie Smith, MIT; NSF Data Management Plan Case Studies; RDAP11 Summit
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Micah Altman, Harvard; Policy-based Data Management
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Data Citation Implementation at DataverseMerce Crosas
Presentation at the Data Citation Implementation Pilot Workshop in Boston, February 3rd, 2016.
https://www.force11.org/group/data-citation-implementation-pilot-dcip/pilot-project-kick-workshop
Publishing the Full Research Data LifecycleAnita de Waard
This document discusses strategies for supporting open science through the full research cycle and data/software preservation. It outlines current practices for managing, storing, publishing, and reusing research data and software. It proposes improvements like requiring researchers to post datasets to repositories under embargo linked to any subsequent publications to reduce workload, better track outputs, and improve data linking and availability. The goal is to make data sharing and open science practices more seamless and effective.
DataONE Education Module 03: Data Management PlanningDataONE
Lesson 3 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Today libraries face more and new challenges when enabling access to information. The growing amount of information in combination with new non-textual media-types demands a constant changing of grown workflows and standard definitions. Knowledge, as published through scientific literature, is the last step in a process originating from primary scientific data. These data are analysed, synthesised, interpreted, and the outcome of this process is published as a scientific article. Access to the original data as the foundation of knowledge has become an important issue throughout the world and different projects have started to find solutions.
Nevertheless science itself is international; scientists are involved in global unions and projects, they share their scientific information with colleagues all over the world, they use national as well as foreign information providers.
When facing the challenge of increasing access to research data, a possible approach should be global cooperation for data access via national representatives:
* a global cooperation, because scientists work globally, scientific data are created and accessed globally.
* with national representatives, because most scientists are embedded in their national funding structures and research organisations.
DataCite was officially launched on December 1st 2009 in London and has 12 information institutions and libraries from nine countries as members. By assigning DOI names to data sets, data becomes citable and can easily be linked to from scientific publications.
Data integration with text is an important aspect of scientific collaboration. DataCite takes global leadership for promoting the use of persistent identifiers for datasets, to satisfy the needs of scientists. Through its members, it establishs and promotes common methods, best practices, and guidance. The member organisations work independently with data centres and other holders of research data sets in their own domains. Based on the work of the German National Library of Science and Technology (TIB) as the first DOI-Registration Agency for data, DataCite has registered over 850,000 research objects with DOI names, thus starting to bridge the gap between data centers, publishers and libraries.
This presentation will introduce the work of DataCite and give examples how scientific data can be included in library catalogues and linked to from scholarly publications.
This presentation sets out some of the challenges around citing and identifying datasets and introduces DataCite, the international data citation initiative. DataCite was founded on 1-December 2009 to support researchers by
providing methods for them to locate, identify, and cite
research datasets with confidence.
This presentation was given by Adam Farquhar at the STM Publishers Association Innovation Conference on 4-Dec-2009.
DataCite – Bridging the gap and helping to find, access and reuse data – Herb...OpenAIRE
OpenAIRE Interoperability Workshop (8 Feb. 2013).
DataCite – Bridging the gap and helping to find, access and reuse data – Herbert Gruttemeier, INIST-CNRS
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
Why would a publisher care about open data?Anita de Waard
A publisher would care about open data for several reasons:
1) Open data increases the value of all parts of the web by allowing programs, not just people, to utilize the data through interconnecting and joining it.
2) Publishers are evolving from linear supply chains focused on content delivery to users, to becoming marketplaces that optimize the number of interactions between users through networked open science.
3) The future of publishing involves networked open science where data is openly accessible, annotated with metadata, and linked together in research objects, increasing findability, accessibility, interoperability, and reusability of research outputs.
This document summarizes work by the RDA/WDS Publishing Data Interest Group to develop a conceptual and practical framework for linking data to literature. It describes the goals of linking research data and publications to increase discoverability, enable proper data reuse, and support attribution. It then outlines a proposed "multi-hub model" infrastructure as an inclusive, standards-based solution. Two key outputs are presented: 1) A prototype "Data-Literature Interlinking" service that has generated over 2 million links, and 2) The Scholix interoperability framework and guidelines for exchanging link data between sources in a standardized way. Participation by sharing link data or helping expand the Scholix standards is encouraged.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This document outlines guidelines for improving the reproducibility of scholarly research through better data citation practices. It recommends depositing cited datasets in archival repositories, using persistent identifiers that meet JDDCP criteria, and having identifiers resolve to landing pages that provide both human- and machine-readable metadata about the dataset. Landing pages should be retained even if the underlying data is removed. Repositories are responsible for maintaining identifier persistence and researchers should treat data as first-class objects in the scholarly process. Following these guidelines would radically improve transparency and enable both humans and machines to access and interpret cited data.
Implementing Archivematica, research data networkJisc RDM
This presentation discusses implementing Archivematica for preserving research data at the University of York and Hull. It covers background on the project, challenges implementing Archivematica, issues with identifying unknown file formats in research data, and future plans to move from proof of concept to production. The project tested pulling metadata from systems into Archivematica for ingest and explored packaging data for long-term preservation and access. A major challenge was the large number of unidentified file formats, which the project is addressing by developing new file format signatures.
This document provides an overview of metadata standards and sources of information about standards. It describes what metadata standards are and their purposes. Sources discussed provide basic information on metadata in general and information on specific standards. The document also describes the Encoded Archival Description (EAD) standard in detail, including its benefits and sources of information about EAD. Finally, it discusses cross-walking between standards and upcoming changes to the EAD standard.
Next-Generation Search Engines for Information RetrievalWaqas Tariq
In the recent years, there have been significant advancements in the areas of scientific data management and retrieval techniques, particularly in terms of standards and protocols for archiving data and metadata. Scientific data is generally rich, not easy to understand, and spread across different places. In order to integrate these pieces together, a data archive and associated metadata should be generated. This data should be stored in a format that can be locatable, retrievable and understandable, more importantly it should be in a form that will continue to be accessible as technology changes, such as XML. New search technologies are being implemented around these protocols, which makes searching easy, fast and yet robust. One such system, Mercury, a metadata harvesting, data discovery, and access system, built for researchers to search to, share and obtain spatiotemporal data used across a range of climate and ecological sciences.
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Rots RDAP11 Data Archives in Federal AgenciesASIS&T
Arnold Rots, VAO; Data Archives in Federal Agencies; RDAP11 Summit
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
This is module 11 in the EDI Data Publishing training course. In this module, you will learn the procedure to upload a data package to the EDI Repository.
Comeaux RDAP11 Data Archives in Federal AgenciesASIS&T
Joey Comeaux, CICL RDA; Data Archives in Federal Agencies
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Smith RDAP11 NSF Data Management Plan Case StudiesASIS&T
MacKenzie Smith, MIT; NSF Data Management Plan Case Studies; RDAP11 Summit
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Micah Altman, Harvard; Policy-based Data Management
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
Data Citation Implementation at DataverseMerce Crosas
Presentation at the Data Citation Implementation Pilot Workshop in Boston, February 3rd, 2016.
https://www.force11.org/group/data-citation-implementation-pilot-dcip/pilot-project-kick-workshop
Publishing the Full Research Data LifecycleAnita de Waard
This document discusses strategies for supporting open science through the full research cycle and data/software preservation. It outlines current practices for managing, storing, publishing, and reusing research data and software. It proposes improvements like requiring researchers to post datasets to repositories under embargo linked to any subsequent publications to reduce workload, better track outputs, and improve data linking and availability. The goal is to make data sharing and open science practices more seamless and effective.
DataONE Education Module 03: Data Management PlanningDataONE
Lesson 3 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Today libraries face more and new challenges when enabling access to information. The growing amount of information in combination with new non-textual media-types demands a constant changing of grown workflows and standard definitions. Knowledge, as published through scientific literature, is the last step in a process originating from primary scientific data. These data are analysed, synthesised, interpreted, and the outcome of this process is published as a scientific article. Access to the original data as the foundation of knowledge has become an important issue throughout the world and different projects have started to find solutions.
Nevertheless science itself is international; scientists are involved in global unions and projects, they share their scientific information with colleagues all over the world, they use national as well as foreign information providers.
When facing the challenge of increasing access to research data, a possible approach should be global cooperation for data access via national representatives:
* a global cooperation, because scientists work globally, scientific data are created and accessed globally.
* with national representatives, because most scientists are embedded in their national funding structures and research organisations.
DataCite was officially launched on December 1st 2009 in London and has 12 information institutions and libraries from nine countries as members. By assigning DOI names to data sets, data becomes citable and can easily be linked to from scientific publications.
Data integration with text is an important aspect of scientific collaboration. DataCite takes global leadership for promoting the use of persistent identifiers for datasets, to satisfy the needs of scientists. Through its members, it establishs and promotes common methods, best practices, and guidance. The member organisations work independently with data centres and other holders of research data sets in their own domains. Based on the work of the German National Library of Science and Technology (TIB) as the first DOI-Registration Agency for data, DataCite has registered over 850,000 research objects with DOI names, thus starting to bridge the gap between data centers, publishers and libraries.
This presentation will introduce the work of DataCite and give examples how scientific data can be included in library catalogues and linked to from scholarly publications.
This presentation sets out some of the challenges around citing and identifying datasets and introduces DataCite, the international data citation initiative. DataCite was founded on 1-December 2009 to support researchers by
providing methods for them to locate, identify, and cite
research datasets with confidence.
This presentation was given by Adam Farquhar at the STM Publishers Association Innovation Conference on 4-Dec-2009.
DataCite – Bridging the gap and helping to find, access and reuse data – Herb...OpenAIRE
OpenAIRE Interoperability Workshop (8 Feb. 2013).
DataCite – Bridging the gap and helping to find, access and reuse data – Herbert Gruttemeier, INIST-CNRS
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
Why would a publisher care about open data?Anita de Waard
A publisher would care about open data for several reasons:
1) Open data increases the value of all parts of the web by allowing programs, not just people, to utilize the data through interconnecting and joining it.
2) Publishers are evolving from linear supply chains focused on content delivery to users, to becoming marketplaces that optimize the number of interactions between users through networked open science.
3) The future of publishing involves networked open science where data is openly accessible, annotated with metadata, and linked together in research objects, increasing findability, accessibility, interoperability, and reusability of research outputs.
This document summarizes work by the RDA/WDS Publishing Data Interest Group to develop a conceptual and practical framework for linking data to literature. It describes the goals of linking research data and publications to increase discoverability, enable proper data reuse, and support attribution. It then outlines a proposed "multi-hub model" infrastructure as an inclusive, standards-based solution. Two key outputs are presented: 1) A prototype "Data-Literature Interlinking" service that has generated over 2 million links, and 2) The Scholix interoperability framework and guidelines for exchanging link data between sources in a standardized way. Participation by sharing link data or helping expand the Scholix standards is encouraged.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This document outlines guidelines for improving the reproducibility of scholarly research through better data citation practices. It recommends depositing cited datasets in archival repositories, using persistent identifiers that meet JDDCP criteria, and having identifiers resolve to landing pages that provide both human- and machine-readable metadata about the dataset. Landing pages should be retained even if the underlying data is removed. Repositories are responsible for maintaining identifier persistence and researchers should treat data as first-class objects in the scholarly process. Following these guidelines would radically improve transparency and enable both humans and machines to access and interpret cited data.
Implementing Archivematica, research data networkJisc RDM
This presentation discusses implementing Archivematica for preserving research data at the University of York and Hull. It covers background on the project, challenges implementing Archivematica, issues with identifying unknown file formats in research data, and future plans to move from proof of concept to production. The project tested pulling metadata from systems into Archivematica for ingest and explored packaging data for long-term preservation and access. A major challenge was the large number of unidentified file formats, which the project is addressing by developing new file format signatures.
This document provides an overview of metadata standards and sources of information about standards. It describes what metadata standards are and their purposes. Sources discussed provide basic information on metadata in general and information on specific standards. The document also describes the Encoded Archival Description (EAD) standard in detail, including its benefits and sources of information about EAD. Finally, it discusses cross-walking between standards and upcoming changes to the EAD standard.
Next-Generation Search Engines for Information RetrievalWaqas Tariq
In the recent years, there have been significant advancements in the areas of scientific data management and retrieval techniques, particularly in terms of standards and protocols for archiving data and metadata. Scientific data is generally rich, not easy to understand, and spread across different places. In order to integrate these pieces together, a data archive and associated metadata should be generated. This data should be stored in a format that can be locatable, retrievable and understandable, more importantly it should be in a form that will continue to be accessible as technology changes, such as XML. New search technologies are being implemented around these protocols, which makes searching easy, fast and yet robust. One such system, Mercury, a metadata harvesting, data discovery, and access system, built for researchers to search to, share and obtain spatiotemporal data used across a range of climate and ecological sciences.
This document introduces LODE-BD (LOD-Enabled Bibliographic Data), a reference tool to help information professionals select appropriate encoding strategies for publishing bibliographic data as linked open data (LOD). LODE-BD provides decision trees to guide the selection of relevant metadata properties and terms from existing standards. It addresses key questions about encoding data for exchange and as LOD, and assists in choosing appropriate terms for different bibliographic properties and entities like titles, subjects, and responsible bodies. The goal is to promote standardized, interoperable LOD-ready bibliographic data.
Metadata for digital long-term preservationMichael Day
Presentation given at the Max Planck Gesellschaft eScience Seminar 2008: Aspects of long-term archiving, hosted by the Gesellschaft für Wissenschaftliche Datenverarbeitung mbh Göttingen (GWDG), Göttingen, Germany, 19-20 June 2008
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”
The document discusses advances in data management practices and technologies for ecosystem science. It describes the role of a data manager in facilitating data management, from collecting raw data to organizing it in standard formats and metadata according to community practices. Well-managed data is stored and shared through repositories to enable discovery, access, interoperability and future reuse. Resources and experts are available to help researchers improve their data management.
This document discusses metadata, including what it is, why it is important, common components of metadata records, examples of metadata standards, and tips for writing good metadata. Metadata captures key details about data, such as who created it, when, how, and why, to facilitate discovering, understanding, and reusing the data. Standards provide consistency for computer interpretation and searching. Good metadata includes specific, accurate, and complete details to fully document data.
Enriching Scholarship 2014 Beyond the Journal Article: Publishing and Citing ...Natsuko Nicholls
The document discusses data sharing policies and mandates from various organizations including federal funding agencies in the US and internationally, journals, and a paradigm shift toward more transparent and collaborative research that integrates publications and data. Key points include requirements for data management plans from NIH and NSF, expectations of funding agencies in other countries to maximize access to research data, a journal policy requiring data to be made available, and challenges around measuring the impact of shared data given the lack of common practices and standards for citing data.
DataCite and its DOI infrastructure - IASSIST 2013Frauke Ziedorn
- DataCite is an international consortium that aims to make research data citable and accessible by establishing a system for minting DOIs (Digital Object Identifiers) for research data.
- DataCite has grown to include 17 member organizations from 12 countries that work with the Technical Information Library (TIB) to register over 1.5 million DOIs for research data.
- The DataCite metadata schema, based on Dublin Core, requires core metadata for DOI registration and encourages linking related publications, data, and other research objects to facilitate discovery and access.
Research Data Management and Sharing for the Social Sciences and HumanitiesRebekah Cummings
This document summarizes a presentation on research data management for social and behavioral sciences and humanities. The presentation covered topics such as what data management is, why it is important to manage and share data, how to create data management plans, organize data files through naming conventions and folder structures, describe data through metadata and codebooks, issues around data ownership, and data storage, archiving and sharing options. The presentation was aimed at providing guidance to researchers at the University of Utah on best practices for managing and sharing their research data.
Research data management (RDM) and the FAIR principles (Findable, Accessible, Interoperable, Reusable) are widely
promoted as basis for a shared research data infrastructure. Nevertheless, researchers involved in next generation
sequencing (NGS) still lack adequate RDM solutions. The NGS metadata is generally not stored together with the raw
NGS data, but kept by individual researchers in separate files. This situation complicates RDM practice. Moreover,
the (meta)data does often not meet the FAIR principles [6]. Consequently, a central FAIR-compliant repository
is highly desirable to support NGS related research. We have selected iRODS (Rule-Oriented Data management
systems) [3] as a basis for implementing a sequencing data repository because it allows storing both data and metadata
together. iRODS serves as scalable middleware to access different storage facilities in a centralized and virtualized
way, and supports different types of clients. This repository will be part of an ecosystem of RDM solutions that
cover complementary phases of the research data life cycle in our organization (Academic Medical Center of the
University of Amsterdam). We selected Virtuoso [5] to enrich the metadata from iRODS to enable the management
of a triplestore for linked data. The metadata in the iCat (iRODS’ metadata catalogue) and the ontology in Virtuoso
are kept synchronized by enforcement of strict data manipulation policies. We have implemented a prototype to
preserve raw sequencing data for one research group. Three iRODS client interfaces are used for different purposes:
Davrods [4] for data and metadata ingestion, data retrieval; Metalnx-web [7] for administration, data curation, and
repository browsing; and iCommands [2] for all tasks by advanced users. Different user profiles are defined (principal
investigator, data curator, repository administrator), with different access rights. New data is ingested by copying raw
sequence files and the corresponding metadata file (a sample sheet) to the landing collection on iRODS. An iRODS
rule is triggered by the sample sheet file, which extracts the metadata and registers it to the iCAT as AVU (Attribute,
Value and Unit). Ontology files are registered into Virtuoso. The sequence files are copied to the persistent collection
and are made uniquely identifiable based on metadata. All the steps are recorded into a report file that enables
monitoring and tracking of progress and faults. Here we describe the design and implementation of the prototype,
and discuss the first assessment results. Initial results indicate that the proposed solution is acceptable and fits the
researchers workflow well.
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
This document summarizes a session from the Force 11 Scholarly Communications Institute Summer School on data discovery. The session covered metadata, including what it is, types of metadata, and standards. It discussed how people search for and find data through various sources. The session also explored the FAIR data principles of findable, accessible, interoperable and reusable data and had breakout groups discuss applying these principles in practice.
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
1. The document discusses the EOSC Dataset Minimum Information (EDMI) approach for exposing research data in the European Open Science Cloud (EOSC).
2. EDMI defines a set of 12 minimum metadata properties to facilitate finding and accessing datasets without being overly descriptive.
3. The approach was developed by engaging EOSC demonstrator data repositories and repositories to propose methods for exposing metadata in a simple and sustainable way.
Meeting the NSF DMP Requirement: March 7, 2012IUPUI
March 7 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
This document discusses metadata and controlled vocabularies. It defines metadata as structured data about an object that supports functions associated with that object. Metadata includes descriptive information about an object's content, context, and structure. Different types of metadata serve administrative, descriptive, preservation, technical, and use functions. Key metadata schemas discussed include Dublin Core, TEI, METS, and MODS. Controlled vocabularies are important for consistency, accuracy, and efficient searching when assigning subject terms in metadata. Quality metadata should be shareable, consistent, and conform to standards.
The document discusses preservation metadata and introduces the PREMIS Data Dictionary. It defines preservation metadata as information used to support the digital preservation process. Key points include:
- Preservation metadata is necessary for understanding and reusing digital information over time.
- The PREMIS Data Dictionary defines a core set of preservation metadata elements and provides strategies for managing this metadata.
- A survey found that while cultural heritage organizations are implementing digital repositories, they have little experience with long-term digital preservation and it is unclear if the metadata being collected will be sufficient.
Similar to EDI Training Module 6: Creating Quality Metadata (20)
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. 2
Background
Data are not inherently self describing. An understanding of what the data are and
how they can be used requires quality metadata (data about data). The level of
metadata quality varies considerably and is a distinguishing feature among data
repositories.
3. 3
Here is the greenish title slide
Objectives
Define metadata and discuss why they are important
Tips for writing quality metadata
Describe the functions of a data repository
4. 4
What are metadata?
Table 1: Average temperature of observation for each species
Courtesy: Viv Hutchison
5. 5
What are metadata?
Table 1: Average temperature of observation for each species
Courtesy: Viv Hutchison
What do temps
represent?
How?
Where?
Units?
6. What are metadata?
Metadata are data about data
WHO created the data?
WHAT is the content of the data?
WHEN were the data created?
WHERE were they collected?
WHY were the data collected?
6
7. Value of Metadata
Essential for making data FAIR
● Findable: Keywords, good title, DOI
● Accessible: Tell user how to access the data or provide direct link to it
● Interoperable: Accurate and well-described methods and attributes
● Reusable: Understandable
7
8. Metadata for EDI (1)
Title and Abstract
Investigators: Synonymous with
"authors" of a paper, where the
investigator is the persons (or in
some case institutions) that have
made an intellectual contribution to
design of the data
collection/creation effort.
License: Tells future data users how
they can re-use the data
8
9. Metadata for EDI (2)
Keywords:
● Important for data discovery.
● Select from an existing
controlled vocabulary or
thesaurus.
Funding:
● Include award number
Timeframe & Location
Taxonomic species
Methods 9
10. Metadata for EDI (3)
Describe each data table:
Column Name
Description
● Standard units: EML metadata has
a set of predefined variable units
(EML unit dictionary).
○ Kg/m2 =
kilogramPerMeterSquared
● Custom units: Any unit not defined
in the dictionary can be included as
custom unit.
Unit/Code Explanation/Date format
Empty Value Code
10
12. EDI Metadata (4)
12
Scripts/code (software): Data
processing and analysis scripts can
be included in a data package.
Data provenance: A record trail
that accounts for the origin of a
dataset.
13. Titles, titles, titles
Titles are critical in helping readers find your data
○ While individuals are searching for the most appropriate datasets, they are
most likely going to use the title as the first criterion to determine if a
dataset meets their needs.
A complete title includes: What, Where, and When (and Who, if relevant)
13
14. Titles, titles, titles
Which title is better?
● Periphyton
● Periphyton Abundance data collected by FCE LTER from Northeast Shark
River Slough, Florida Everglades National Park, from September 2006 to
September 2008
14
18. Ecological Metadata Language (EML)
Metadata standard used widely in US ecological community
Implemented in the Extensible Markup Language (XML)
18
<title>Water Quality Data from Shark River
Slough, Everglades National Park</title>
<originator>
<firstName>Evelyn</lastName>
<lastName>Gaiser</lastName>
</originator>
<method>Grab samples of water were
collected monthly </method>
<date>
<begin>2000-06-01</begin>
<end>2017-03-30</begin>
</date>
19. What does one do with an EML document?
Deposit metadata and data in a data repository!
A data repository is a service operated by research organizations, where research
materials are stored, managed and made accessible
19
20. Data Repositories ensure
● Long-term security of the data
● Long-term accessibility of the data
● Data integrity
● Data discovery
● Datasets are citable
● Most repositories provide a DOI
20
21. Where to deposit ecological data?
Domain specific repositories
● Environmental Data Initiative Repository
● Knowledge Network for Biocomplexity
● Arctic Data Center
Generalist repositories
● Dryad
● Figshare
● Zenodo
Institutional repositories
21
22. Lots of repositories to choose from….
Repositories differ:
● Amount of metadata required
● Support of provenance
● Immutability
● Domains supported
22
26. 26
Here is the greenish title slide
Summary
A metadata record captures critical information about the content of a dataset
Metadata allow data to be discovered, accessed, integrated and re-used
Data repositories support Findability, Accessibility, Interoperability, and Reusability
(FAIR) of research data
Editor's Notes
Describe functions of a data repository which is the final destination of the metadata.
What are metadata? Let’s take a look at this question from the perspective of a researcher. Suppose you are a scientist who wants to study the effects of temperature on frogs. You reach out to all your frog scientist friends and ask for datasets on this topic because you want to do a metaanalysis, an analysis across multiple studies. You are sent this data file by one colleague, with no supporting info. What additional information would you need in order to use these data?
Units?
What do these temperatures represent? Temperature of the skin of the frog or water it was found in?
How were the data collected? Where? In the wild, or in a zoo?
When were the data collected? Was it 30 years ago before amphibians were in decline?
Furthermore, Was the minimum temperature for one of these poor Wood Frogs really zero?
Metadata are just data about data. They help the original creator of the data remember what they did, and they help a secondary data user to understand the data well enough to reuse them. So metadata include information about who created the dataset. A secondary data user may want to contact this creator for more information. What is the content of the dataset? The abstract in the metadata should briefly describe this. When were the data collected? Are the data from a long-term study, or just a short experiment? Where were the measurements collected? How were they collected? Why were the data collected? This Why question may indicate that there was some bias in how measurements were made that make the data unsuitable for a new purpose. So metadata are the who, what, when, where and why of a dataset.
Relative to the value of metadata, You will recall the FAIR data principles that Susanne described on Tuesday. The FAIR principles are guidelines for making data findable, accessible, interoperable and reusable. Metadata are essential to all four of the FAIR principles.
With respect to data findability, Metadata contain keywords, a good title, and a persistent identifier or DOI. All of these facilitate data discovery. Metadata tell a user how to access the data or provide a direct link to it. They indicate how the data are licensed and what a reuser may do with them. Very detailed metadata include accurate and well-described methods and attributes, which are essential for interoperability and integration of datasets. Finally, complete metadata should make the data understandable to a secondary user, without that user needing to contact the data creator.
Speaking of complete and detailed metadata, let’s talk a bit about what metadata EDI requires. This is the Word template for EDI metadata that you may already have seen. I will step you through what it is needed to complete this document. Remember, if you are filling out this template, you need to provide answers to the questions that a typical data reuser would need to in order to interpret these data correctly.
The License you choose will tell future data users how they can reuse the data.
Creative Commons is an American non-profit organization devoted to expanding the range of open access creative works available for others to build upon legally and to share. The organization has released several copyright-licenses, known as Creative Commons licenses, free of charge to the public. CC0 = no rights reserved. Same as CC-BY is a license that requires that the data authors get attribution, but the data can be used however someone likes.
If you don’t choose either one of these licenses, then by default your data set will be given the cc0 license.
On the next page is the section to provide keywords. We suggest that metadata creators select several keywords that are highly relevant to the data being documented. Keywords help a would-be secondary user of the data find the data. Keywords should be precise. Sometimes people get carried away and include 40 keywords. That’s too many. My rule of thumb is 10 or fewer from the LTER CV, and a couple additional ones that describe the project.
Link to a tool to which you can input the abstract of a dataset, for example, and the tool will suggest keywords from the LTER Controlled Vocabulary.
Providing a reference to the funding source for the study is important. Funders like to be able to search a data repository and see what their funding dollars bought. If you provide a grant number and funder id, then NSF, for example, can quickly find datasets related to projects they funded.
Timeframe, Geographic Location
In Methods, you should describe what you did so that someone else could reproduce your study. You should describe experimental design, instruments used, how samples were processed. You can point to published protocols, too, if they are relevant. Methods are really important when a data reuser is trying to determine is the data are suitable for their analysis or not.
Here is where you describe all the attributes in a data table. In the first column, you would put the variable or attribute names from the header of your dataset.
Units have to written in a particular way. Units get written out in camelCase so that they are unambiguous.
Example of data from long-term stream chemistry study.
Data packages don’t always contain just data and metadata. They may contain scripts that were used to process the data in some way. If you generated code while manipulating the dataset and quality controlling it, you can include the code in the data package.
Finally, data provenance can be described. Data provenance refers to a record trail that accounts for the origin of the dataset. If the frog researcher integrated 15 frog datasets from other researchers into a single dataset for her study, then this is where the identity of those original datasets can be recorded. Important for supporting reproducible science.
I will now offer you a few tips on how to create quality metadata, starting with what a good title should contain.
Select keywords wisely. Keywords aren’t something you should just pull out of the air. It’s better to choose terms from a thesaurus or controlled vocabulary. A controlled vocabulary is a standardized list of words that provide a consistent way to describe and index data. In the case of the LTER Controlled Vocabulary, the list consists of about 700 terms that ecologists use frequently to keyword data. So, How you would use the Controlled Vocabulary? If you are considering using CO2 as your keyword, for instance, you would look into this controlled vocabulary and see if CO2 is there, and it is, but it is not the preferred term. The words carbon dioxide should be written out, rather than entering CO2 as the keyword. By using these standard terms, it’s possible to index data holdings based on these terms. This improves the potential for data discovery considerably.
Also, it can be helpful to have a reference for standardized place names. Sometimes you may get data that contain specific place names that are likely to be expressed in a variety of different ways. For instance, in the Everglades there are these “Conservation Areas” that have received different treatments. Metadata for these areas may say the research site is “Conservation area 3” or WMACA 3 or other permutations. To get the standardized name, I consult this gazetteer. It’s a lot easier to find data for these locations if all datasets use the same version of the place name.
So you’ve written some brilliant metadata. Then what happens? Well, The Word template isn’t machine readable. Computers like more structure than a Word document can offer. You will learn later today how to generate structured metadata from the EDI template. The structured metadata standard we use at EDI is called Ecological Metadata Language. EML was developed for documenting ecological and environmental datasets, and is implemented in XML. This blue box shows a fragment of EML. You can see that elements of the metadata are enclosed in tags that describe their content. These tags are the XML, in the simplest possible sense. Having the data in EML makes it machine-readable. You can throw 1000 EML documents at a computer and request all the titles be output, and the computer can do that easily.
Once you have your clean dataset and your EML, what do you do with it? You are ready to share data through the EDI Repository. A data repository is a service operated by research organizations where research materials are stored, managed, and made accessible.
What is special about a data repository as opposed to sharing your data and metadata on a lab web page, or a field station’s website. Data repositories have some important functions that a lab website does not.
For instance, Repositories provide for the Long-term security of the data, meaning that a dataset will not ever be lost from a repository. It will be available 20 or more years after it is deposited.
Repositories ensure Long-term accessibility of data. : A dataset will always be retrievable from the repository.
Data integrity is preserved in a repository, meaning the data set will never be changed while in the repository. Data is said to be immutable.
For Data discovery: The repository will offer a mechanism by which to find data.
Datasets in a repository are citeable: datasets in most repositories receive a DOI, Digital object identifier, which provides a persistent link to a dataset’s location on the Internet.
You won’t get a DOI by posting your data on your lab website, and DOIs are what makes it possible for researchers to get credit from citations of their data.
Is EDI the only place to store ecological data? No there are many repositories that will accept ecological data. There are three kinds of repositories. Domain-specific, generalist, and institutional. Domain specific repositories store data from different domains, for example ecological data, physics data, sociological data, and all the rest. Repositories specifically for ecological data in the US include: KNB, Arctic Data center. Many other ecological repositories in other countries. Generalist repositories are designed to accept any kind of data. Institutional repositories are found at large institutions which now run their own repositories to store data, reports, articles, photos, all kinds of products from researchers at the institution. Some researchers prefer to store their data in their institutional repository.
RE3data.org: 2,540 repositories indexed by this service. Neotoma (paleoecological data), Gulf Coast Repository, VertNet, Fish Database of Taiwan, Australian Waterbird Surveys,
Let’s take a look at a data record in the EDI Repository so you can see how the structured metadata is turned into a nice html display.
Data are cited alongside journal citations in the references section of a paper.
These columns represent the columns in the dataset. Look at the detail here! Because the data are described so carefully, it’s possible to write on-the-fly R code or Python code that will directly extract this data table from the repository and import it into R.