This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
Open Research Data: Licensing | Standards | FutureRoss Mounce
This document provides an overview of open research data, including definitions, licensing, standards, and history. It defines open data as data that anyone can freely access, use, modify, and share with few restrictions. For data to be truly open, it recommends using a CC0 public domain waiver or an attribution-only license. It discusses issues with non-commercial and no derivatives restrictions. The document also provides guidance on technical aspects like recommended file formats and standards. It briefly summarizes the history of data sharing, from centralized data centers to online supplementary data to emerging data paper journals. The key messages are that data should be FAIR (Findable, Accessible, Interoperable, Reusable) and that open data benefits both
Jonathan Tedds Distinguished Lecture at DLab, UC Berkeley, 12 Sep 2013: "The ...Jonathan Tedds
This document discusses open access to research data and peer review of data publications. It notes that as a first step, data underpinning journal articles should be made concurrently available in accessible databases. The Royal Society report in 2012 advocated for all science literature and data to be online and interoperable. Key issues in linking data to the scientific record are data persistence, quality, attribution, and credit. The document provides examples from astronomy of data reuse leading to new publications and cites a study finding poor reproducibility of ecological data sets over time as data availability declines. It outlines different levels of research data from raw to processed to published and discusses initiatives for open data publication and peer review.
The document discusses the future of research communications and some of the drivers shaping it, including technological advances, funding levels, and the needs of the scientific community. It touches on a history of scientific publishing and culture. The core idea is that successful future systems will function as interconnected ecosystems where people, technologies, publications, and data all work together to support argument, evidence, and the scientific social process.
Published on Jan 29, 2016 by PMR
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuous Integration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of content mining (TDM)
This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
Open Research Data: Licensing | Standards | FutureRoss Mounce
This document provides an overview of open research data, including definitions, licensing, standards, and history. It defines open data as data that anyone can freely access, use, modify, and share with few restrictions. For data to be truly open, it recommends using a CC0 public domain waiver or an attribution-only license. It discusses issues with non-commercial and no derivatives restrictions. The document also provides guidance on technical aspects like recommended file formats and standards. It briefly summarizes the history of data sharing, from centralized data centers to online supplementary data to emerging data paper journals. The key messages are that data should be FAIR (Findable, Accessible, Interoperable, Reusable) and that open data benefits both
Jonathan Tedds Distinguished Lecture at DLab, UC Berkeley, 12 Sep 2013: "The ...Jonathan Tedds
This document discusses open access to research data and peer review of data publications. It notes that as a first step, data underpinning journal articles should be made concurrently available in accessible databases. The Royal Society report in 2012 advocated for all science literature and data to be online and interoperable. Key issues in linking data to the scientific record are data persistence, quality, attribution, and credit. The document provides examples from astronomy of data reuse leading to new publications and cites a study finding poor reproducibility of ecological data sets over time as data availability declines. It outlines different levels of research data from raw to processed to published and discusses initiatives for open data publication and peer review.
The document discusses the future of research communications and some of the drivers shaping it, including technological advances, funding levels, and the needs of the scientific community. It touches on a history of scientific publishing and culture. The core idea is that successful future systems will function as interconnected ecosystems where people, technologies, publications, and data all work together to support argument, evidence, and the scientific social process.
Published on Jan 29, 2016 by PMR
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuous Integration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of content mining (TDM)
Specimen-level mining: bringing knowledge back 'home' to the Natural History ...Ross Mounce
A talk given at the Geological Society of London, UK on 2016/03/09 as part of the Lyell meeting on Palaeoinformatics. http://www.geolsoc.org.uk/lyell16 #lyell16
NPG Scientific Data; SSP, Boston, May 2014: http://www.sspnet.org/events/annu...Susanna-Assunta Sansone
This document outlines the services provided by Scientific Data, a publication from Nature that helps authors publish, discover, and reuse research data. It provides structured metadata and a narrative component for Data Descriptors, which describe datasets in detail without new scientific findings. The publication works with over 50 repositories and provides submission assistance and semantic annotation to help authors find appropriate data archiving locations.
Liberating facts from the scientific literature - Jisc Digifest 2016 TheContentMine
Published on Mar 4, 2016 by PMR
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
This document summarizes a presentation given by Peter Murray-Rust on open science. Some key points from the presentation include:
- Open science and open data are essential for young researchers and students to have the freedom to conduct research and change the world.
- Content mining scientific literature is important but publishers are attempting to control open data and restrict access, which hampers research progress.
- Past student movements have fought for openness and freedom in research, and new approaches may be needed now to change laws and policies to allow content mining while making all research outputs openly available.
This document discusses challenges with the current scientific publishing system and proposes a vision for next generation scientific publishing (NGSP). Some key problems include retractions due to misconduct, lack of reproducibility, and non-reusable data and methods. NGSP would feature transparent and computable data and methods, open annotation of narratives and objects, and no restrictions on text mining or remixing. It would move information more quickly and allow verification through an open, service-oriented system without walled gardens. Taking NGSP forward will require collaboration across stakeholders in research communications.
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
Enabling Precise Identification and Citability of Dynamic Data: Recommendatio...LEARN Project
Enabling Precise Identification and Citability of Dynamic Data: Recommendations of the RDA Working Group, by Andreas Rauber – 2nd LEARN Workshop, Vienna, 6th April 2016
The document discusses the MESUR (Making Use and Sense of Scholarly Usage Data) project which aims to develop new metrics for scholarly impact and prestige based on usage data from digital scholarly resources rather than just citations. The key points are:
1) MESUR analyzes over 1 billion usage events of scholarly articles and develops network-based metrics from usage patterns to map the structure of science.
2) Preliminary results show relevant structure in usage-based network maps that correlate with traditional citation-based metrics.
3) MESUR has produced a variety of usage and citation-based metrics and developed online tools for exploring these metrics.
Automatic Extraction of Knowledge from the Literaturepetermurrayrust
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
NPG Scientific Data - Metabolomics Society meeting, Tsuruola, Japan, 2014Susanna-Assunta Sansone
This document provides information about Scientific Data, an online publication from Nature Research that publishes peer-reviewed descriptions of scientifically valuable datasets. It summarizes the goals of Scientific Data, which are to promote data sharing, reuse, and reproducibility. The document outlines the structured format for Data Descriptors, which include both a narrative component and experimental metadata. It describes the peer review process, which focuses on data quality, completeness of description, and potential for reuse rather than novelty of findings. Finally, it provides examples of diverse current content and encourages collaboration with data repositories.
The document describes the SFX framework for context-sensitive reference linking, which allows a user accessing a citation to be redirected to an appropriate full text or service based on their context. The framework uses an OpenURL standard to pass citation metadata from a link source to a parsing server, which then sends the metadata to a linking server to determine the most relevant services and create dynamic links to them based on the user's access and the available library collections and resources. The goal is to provide context-sensitive services to users based on their access and the cited item metadata rather than relying on pre-computed static links.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
The document discusses best practices for preparing data for open publication. It recommends thinking openly and planning early by creating detailed data management plans. It provides examples of repositories like GenBank, ClinicalTrials.gov, FlyBase, Figshare, and Dryad that accept different types of data. The document emphasizes documenting data thoroughly with metadata and standards and following ethical guidelines for sharing and preserving data in the long term.
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Open access for researchers, policy makers and research managers - Short ver...Iryna Kuchma
Presented at Open Access: Maximising Research Impact, April 23 2009, New Bulgarian University Library, Sofia. Open access for researchers: enlarged audience, citation impact, tenure and promotion. Open access for policy makers and research managers:
new tools to manage a university’s image and impact. How to maximize the visibility of research publications, improve the impact and influence of the work, disseminate the results of the research, showcase the quality of the research in the Universities and research institutions, better measure and manage the research in the institution, collect and curate the digital outputs, generate new knowledge from existing findings, enable and encourage collaboration, bring savings to the higher education sector and better return on investment. What are the key functions for research libraries?
We describe current work in federating data from institutional research profiling systems – providing single-point
access to substantial numbers of investigators through concept-driven search, visualization of the relationships
among those investigators and the ability to interlink systems into a single information ecosystem.
1. The document discusses research networking profiles created by the Clinical and Translational Science Institute at the University of California, San Francisco (CTSI at UCSF).
2. It notes that most universities have their own research networking profiles, like LinkedIn for researchers, to provide credibility and allow customization.
3. However, the document advocates connecting local profiles into a global research network using Linked Open Data, OAuth authentication, and OpenSocial technologies to facilitate collaboration between researchers across institutions.
The document discusses the W3C Open Annotation Data Model group and their work developing an interoperable data model for annotations. It aims to allow annotations of digital resources to be portable, aggregated, and shared across different applications and platforms. The group brings together the Annotation Ontology and Open Annotation Collaboration efforts to define a common model. The model defines the basic components of an annotation - body and target - and provides examples of use cases like bookmarking, commenting, and annotating text fragments and media.
This document provides an overview of open science and how to practice open science. It defines open science as research carried out and communicated in a way that allows others to contribute and collaborate. The benefits of open science include increased visibility, citations, and economic benefits when data is freely available. It recommends publishing papers through open access routes, sharing data and code openly with permissive licenses, and depositing outputs in repositories to practice open science. The document provides guidance on choosing file formats, metadata standards, and repositories to openly share research outputs.
This document discusses open science and research. It defines open science as making research transparent and accessible at all stages of the research process through open access, open data, open source code and open notebooks. It outlines the key elements of open science like open access publishing, open data repositories, open source software, citizen science and more. It also discusses open science initiatives in Europe, Africa and South Africa and the need for urgent policy actions to promote open science.
Specimen-level mining: bringing knowledge back 'home' to the Natural History ...Ross Mounce
A talk given at the Geological Society of London, UK on 2016/03/09 as part of the Lyell meeting on Palaeoinformatics. http://www.geolsoc.org.uk/lyell16 #lyell16
NPG Scientific Data; SSP, Boston, May 2014: http://www.sspnet.org/events/annu...Susanna-Assunta Sansone
This document outlines the services provided by Scientific Data, a publication from Nature that helps authors publish, discover, and reuse research data. It provides structured metadata and a narrative component for Data Descriptors, which describe datasets in detail without new scientific findings. The publication works with over 50 repositories and provides submission assistance and semantic annotation to help authors find appropriate data archiving locations.
Liberating facts from the scientific literature - Jisc Digifest 2016 TheContentMine
Published on Mar 4, 2016 by PMR
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
This document summarizes a presentation given by Peter Murray-Rust on open science. Some key points from the presentation include:
- Open science and open data are essential for young researchers and students to have the freedom to conduct research and change the world.
- Content mining scientific literature is important but publishers are attempting to control open data and restrict access, which hampers research progress.
- Past student movements have fought for openness and freedom in research, and new approaches may be needed now to change laws and policies to allow content mining while making all research outputs openly available.
This document discusses challenges with the current scientific publishing system and proposes a vision for next generation scientific publishing (NGSP). Some key problems include retractions due to misconduct, lack of reproducibility, and non-reusable data and methods. NGSP would feature transparent and computable data and methods, open annotation of narratives and objects, and no restrictions on text mining or remixing. It would move information more quickly and allow verification through an open, service-oriented system without walled gardens. Taking NGSP forward will require collaboration across stakeholders in research communications.
Talk to EBI Industry group on Open Software for chemical and pharmaceutical sciences. Covers examples of chemistry , wit demos, and argues that all public knowledge should be Openly accessible
Enabling Precise Identification and Citability of Dynamic Data: Recommendatio...LEARN Project
Enabling Precise Identification and Citability of Dynamic Data: Recommendations of the RDA Working Group, by Andreas Rauber – 2nd LEARN Workshop, Vienna, 6th April 2016
The document discusses the MESUR (Making Use and Sense of Scholarly Usage Data) project which aims to develop new metrics for scholarly impact and prestige based on usage data from digital scholarly resources rather than just citations. The key points are:
1) MESUR analyzes over 1 billion usage events of scholarly articles and develops network-based metrics from usage patterns to map the structure of science.
2) Preliminary results show relevant structure in usage-based network maps that correlate with traditional citation-based metrics.
3) MESUR has produced a variety of usage and citation-based metrics and developed online tools for exploring these metrics.
Automatic Extraction of Knowledge from the Literaturepetermurrayrust
ContentMine tools (and the Harvest alliance) can be used to search the literature for knowledge, especially in biomedicine. All tools are Open and shortly we shall be indexing the complete daily scholarly literature
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
NPG Scientific Data - Metabolomics Society meeting, Tsuruola, Japan, 2014Susanna-Assunta Sansone
This document provides information about Scientific Data, an online publication from Nature Research that publishes peer-reviewed descriptions of scientifically valuable datasets. It summarizes the goals of Scientific Data, which are to promote data sharing, reuse, and reproducibility. The document outlines the structured format for Data Descriptors, which include both a narrative component and experimental metadata. It describes the peer review process, which focuses on data quality, completeness of description, and potential for reuse rather than novelty of findings. Finally, it provides examples of diverse current content and encourages collaboration with data repositories.
The document describes the SFX framework for context-sensitive reference linking, which allows a user accessing a citation to be redirected to an appropriate full text or service based on their context. The framework uses an OpenURL standard to pass citation metadata from a link source to a parsing server, which then sends the metadata to a linking server to determine the most relevant services and create dynamic links to them based on the user's access and the available library collections and resources. The goal is to provide context-sensitive services to users based on their access and the cited item metadata rather than relying on pre-computed static links.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
The document discusses best practices for preparing data for open publication. It recommends thinking openly and planning early by creating detailed data management plans. It provides examples of repositories like GenBank, ClinicalTrials.gov, FlyBase, Figshare, and Dryad that accept different types of data. The document emphasizes documenting data thoroughly with metadata and standards and following ethical guidelines for sharing and preserving data in the long term.
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Open access for researchers, policy makers and research managers - Short ver...Iryna Kuchma
Presented at Open Access: Maximising Research Impact, April 23 2009, New Bulgarian University Library, Sofia. Open access for researchers: enlarged audience, citation impact, tenure and promotion. Open access for policy makers and research managers:
new tools to manage a university’s image and impact. How to maximize the visibility of research publications, improve the impact and influence of the work, disseminate the results of the research, showcase the quality of the research in the Universities and research institutions, better measure and manage the research in the institution, collect and curate the digital outputs, generate new knowledge from existing findings, enable and encourage collaboration, bring savings to the higher education sector and better return on investment. What are the key functions for research libraries?
We describe current work in federating data from institutional research profiling systems – providing single-point
access to substantial numbers of investigators through concept-driven search, visualization of the relationships
among those investigators and the ability to interlink systems into a single information ecosystem.
1. The document discusses research networking profiles created by the Clinical and Translational Science Institute at the University of California, San Francisco (CTSI at UCSF).
2. It notes that most universities have their own research networking profiles, like LinkedIn for researchers, to provide credibility and allow customization.
3. However, the document advocates connecting local profiles into a global research network using Linked Open Data, OAuth authentication, and OpenSocial technologies to facilitate collaboration between researchers across institutions.
The document discusses the W3C Open Annotation Data Model group and their work developing an interoperable data model for annotations. It aims to allow annotations of digital resources to be portable, aggregated, and shared across different applications and platforms. The group brings together the Annotation Ontology and Open Annotation Collaboration efforts to define a common model. The model defines the basic components of an annotation - body and target - and provides examples of use cases like bookmarking, commenting, and annotating text fragments and media.
This document provides an overview of open science and how to practice open science. It defines open science as research carried out and communicated in a way that allows others to contribute and collaborate. The benefits of open science include increased visibility, citations, and economic benefits when data is freely available. It recommends publishing papers through open access routes, sharing data and code openly with permissive licenses, and depositing outputs in repositories to practice open science. The document provides guidance on choosing file formats, metadata standards, and repositories to openly share research outputs.
This document discusses open science and research. It defines open science as making research transparent and accessible at all stages of the research process through open access, open data, open source code and open notebooks. It outlines the key elements of open science like open access publishing, open data repositories, open source software, citizen science and more. It also discusses open science initiatives in Europe, Africa and South Africa and the need for urgent policy actions to promote open science.
- Research infrastructures enable better science by building a common vision, allowing scientists to seamlessly share resources, applying economies of scale, and constructing new resources from combinations of shared ones.
- Open science means broader access to publicly funded research results through open access publications, data, software, methodologies, and more. This helps build on previous work, avoid duplication, speed innovation, and involve citizens.
- The European Commission's open access mandate requires beneficiaries to make publications and underlying data openly available, with possible sanctions for non-compliance like payment suspensions. Research infrastructures and open science publishing aim to increase transparency, reproducibility, and reuse of research outputs.
An introduction to open science, why it's important and how to do it. This presentation was given at the European Medical Students Association (EMSA) event, 'Open Access in Action' in Berlin on 14th-15th September 2015
Open science refers to making scientific research and data accessible to all. It includes open access to publications, open data, open source software, open notebooks, and citizen science. The European Union supports open science to increase transparency, collaboration and innovation in research. A workshop was held in South Africa to help develop an open science policy, with feedback that the policy will be finalized in September 2018 after additional workshops with European Union involvement. Open science aims to make the entire research process publicly available and reusable to maximize scientific progress.
The Culture of Research Data, by Peter Murray-RustLEARN Project
1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Peter Murray-Rust, ContentMine.org and University of Cambridge
The document discusses open science, which aims to make scientific research, data, and communication accessible to all levels of society. Open science includes practices like publishing open research, advocating for open access, and making it easier to share scientific knowledge. It involves transparency in methodology, public availability and reusability of data, and using online tools to facilitate collaboration. The document outlines some challenges in the current scientific community like high publication and subscription costs and closed databases. It also discusses directions for open science like open databases and platforms, publications, methodology, and software as well as open notebook science.
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuousIntegration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of contentmining (TDM)
Being an Open Scholar in a Connected WorldStian Håklev
This document discusses the benefits of open scholarship in a connected world. It argues that open access to research articles makes information more accessible to broader audiences, including the general public and students. When data and research notes are openly shared online, it can enable unexpected reuse and collaboration. However, the current academic publishing and reward systems may not fully incentivize open scholarship. The document calls for exploring new models of peer review, metrics of impact, and ways of publishing research to make the scholarly process more transparent and collaborative.
Open Science: Openness in Scientific Researchpedjac
The document discusses open science and defines it as research carried out and communicated in a manner that allows others to contribute, collaborate, and build upon the work by making all data, results, and protocols freely available. It outlines traditional norms of science like communalism and skepticism, but also notes counter-norms like secrecy and dogmatism that have emerged. The rise of intellectual property rights and industry-science relations are discussed in relation to debates around open versus closed models of science. The benefits of openness include efficiency, accessibility, avoiding duplications, and enabling new collaborations. However, motivations for individual researchers may not always align with openness due to the current research evaluation system. A survey of researchers found concerns around
Open Research comprises open access to the broad range of research outputs, from journal articles and the underlying data to protocols, results (including negative results), software and tools. Open Research increases inclusivity and collaboration, improves transparency and reproducibility of research and underpins research integrity.
This workshop focuses on the benefits of practicing open research for you as a researcher, to improve discoverability and maximise access to your work and to raise your professional profile.
By the end of the session you will:
• Have an understanding of the principles of Open Research
• Understand open licences and how they apply to publications, data and software
• Be able to apply key tools and techniques to increase the visibility of yourself and your research, including repositories, ORCID, social media and altmetrics
• Describe the different ways of making research and data available open access
This review demonstrates that using these websites can provide researchers with valuable sources of data and research, facilitating access to current literature and specialized scientific content. For optimal results, diversifying sources of research and using multiple search engines based on need and specialization is recommended
Slides describing Force11 Work and background of several of the speakers, used for talks to University of Lethbridge, Carnegie Mellon and to Elsevier internally
A open science presentation focusing on the benefits to be gained and basic practices to follow. This was given on behalf of FOSTER at the Open Science Boos(t)camp event at KU Leuven on 24th October 2014.
The world of research data: when should data be closed, shared or openheila1
That research data should be shared with the rest of the world has become almost evangelical in nature. This paper will try to answer the following questions:
• What are the (real) reasons for ‘forcing’ scientists to open their data, even if they are not ready to do so?
• What right have non-scientists (and scientists) to push indiscriminately for the sharing of data without taking the nuances of research into consideration?
Physical characteristics of research data before it can be shared
Modes of data sharing
Case study: public humiliation in the name of Open Science
Advantages and disadvantages of sharing research data
AI to the rescue of open research articles?
In conclusion
OSFair2017 Training | Best practice in Open ScienceOpen Science Fair
Iryna Kuchma talks about best practices in Open Science.
Workshop title: Fostering the practical implementation of Open Science in Horizon 2020 and beyond
Workshop overview:
This workshop will showcase some of the elements required for the transition to Open Science: services and tools, policies as guidance for good practices, and the roles of the respective actors and their networks.
DAY 2 - PARALLEL SESSION 4 & 5
Open Data (and Software, and other Research Artefacts) -A proper managementOscar Corcho
Presentation at the event "Let's do it together: How to implement Open Science Practices in Research Projects" (29/11/2019), organised by Universidad Politécnica de Madrid, where we discuss on the need to take into account not only open access or open research data, but also all the other artefacts that are a result of our research processes.
This document discusses challenges around scholarly data, including fragmented and poorly described data. It emphasizes the importance of experimental details, data availability, and data publication for reproducibility. Springer Nature's Scientific Data is highlighted as a new open-access journal for detailed data descriptors. The Scientific Data ISA-explorer is presented as a web application for discovering, exploring and visualizing data descriptors.
Similar to Open Science : Democratizing Access to Science (20)
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
7. - Authors are not paid for papers they publish!
- Editorial board (often) receives no monetary compensation
for reviewing.
- Research projects are (mostly) publicly funded.
- Public Institutions have to pay to access research
articles.
Doing research: Literature review
- Content created by research scientists. عندككة؟فلو
هامانجمشَهمكفن
We need a solution, please!!!!
8. Doing research: Literature review
OPEN ACCESS(OA) *: “literature is digital, online,
free of charge, and free of most copyright and
licensing restrictions”.
*Suber, P. Open Access; MIT Press: Cambridge, MA, USA, 2012; Chapter 1.
Available online: http://mitpress.mit.edu/books/open-access
9. Doing research: Literature review
Cost of Open Access publishing:
*Table4: H. Morrison, J. Salhab, A. Calvé-Genest, and T. Horava, “Open Access Article Processing
Charges: DOAJ survey May 2014,” Publications, vol. 3, no. 1, pp. 1–16, 2015.
10. Doing research: Literature review
Golden Open Access*: peer-reviewed journals that conduct
peer-reviewing and often charges authors for publication.
*Suber, P. Knoweldge Unbound; MIT Press: Cambridge, MA, USA,2016;
https://mitpress.mit.edu/books/knowledge-unbound
2 types of OA:
Green Open Access*: repositories that host pre-prints or
free to access. (e.g.: ArXiv).
11. Doing research: Literature review
Open Access criticism:
- “Double dipping”: charging both subscriptions and OA, a business model
adopted by some Hybrid Access Journals.
- Watering-down science by encouraging low-quality science publication
(predatory Open Access publishers: Pay to Publish).
- Misinterpretation of research findings.
12. Doing research: Literature review
Checklist:
✓ Research papers
- [locked item, proceed to unlock]
- [locked item, proceed to unlock]
The Student is happy!
14. Doing research: Experimentation
- Research studies require data collection and analysis for hypotheses testing
and the investigation of novel methods.
- Research equipment is expensive, requires maintenance and upgrades.
- Laboratories budget cannot afford access to multiple commercial datasets.
- يالالماتريلحقروطار
15. Doing research: Data
OPEN DATA*: “Open Data is research data that is freely available on the
internet permitting any user to download, copy, analyze, re-process,
pass to software or use for any other purpose without financial, legal, or
technical barriers other than those inseparable from gaining access to the
internet itself.”
* https://sparcopen.org/open-data/
16. Doing research: Data
Open Data repositories
- Community driven
project.
- Public data sources in
30 subjects :
● Agriculture
● Biology
● Climate + Weather
● EarthScience
● Economics
● ....
https://github.com/awesomedata/awesome-public-datasets
17. Doing research: Data
Open Data repositories
- EU funded project.
- General purpose OA
repository:
○ Up to 50 GB of free
space per dataset.
○ All research output
accepted.
https://zenodo.org
18. Doing research: Data
Open Data criticism:
The Skeptic
- Gaining profit from the labour of scientists.
- Privacy concerns.
- Misinterpretation and misuse of shared data.
21. Doing research: Software for Data Analysis
- Proprietary research software licenses are expensive.
- Reinventing the wheel : re-implementing data analysis
methods minimizes time dedicated to significant
research.
- Software developers shortage in research
laboratories.
22. Doing research: Source code
OPEN SOURCE*: “programmers or users can read,
modify and redistribute the source code of a piece of
software.”
*F. Pereira, “The Need for Open Source Software in Machine Learning,” vol. 8, pp. 2443–2466, 2007.
23. Doing research: Source code
Open Source Software licence
F. Pereira, “The Need for Open Source Software in Machine Learning,” vol. 8, pp. 2443–2466, 2007.
24. Doing research: Source code
Open Source hosting services
- Control Version System (CVS) based services :
- Github, Gitlab, BitBucket.
- Private hosts:
- Institutions dedicated servers: this-uni.edu/lab/repo/project
- Research oriented : RunMyCode
- Allows to share source code and data associated
with a research publication
25. Doing research: Source code
Open Source for science
- Python programming language ecosystem
26. Doing research: Source code
Open Source scientific computing environment
- Anaconda : open source freemium scientific Python distribution
includes:
- Python 2.x & 3.x interpreters + package manager.
- +1,000 Anaconda-curated and community packages.
- IDEs: Jupyter, Spyder, Rstudio.
- +6 million users.
27. Doing research: Source code
Sponsoring Open Source
NumFocus (Nonprofit organization) :
- Provides fiscal sponsorship for Open
source scientific data projects.
- Sponsored by : IBM, Microsoft,
Bloomberg, Anaconda, Intel, ...
- 21 projects sponsored (so far).
28. Doing research: Source code
Open Source criticism:
The Skeptic
- Software companies benefits Open Source
software with 0 costs and 0 compensation.
- Bad documentation and weak support.
- Issues with Open Source licenses compatibility.
29. Doing research: Data analysis
Checklist:
✓ Research papers
✓ Dataset
✓ Source code
The Student is happy!
32. Open Science
S. Bartling and S. Friesike, “Open Science :One Term, Five Schools of Thought”, Opening Science The Evolving Guide on How the Int
Changing Research, Collaboration and Scholarly Publishing. 2014.
34. Open Science
Reproducibility, what for ?
*Hugo Larochelle, “Some Opinions on Reproducibility in ML”,Reproducibility Workshop, ICML 2017.
- Validating research findings : “If it isn’t reproducible, it might just as well not exist!”.*
- Accelerating novel research discoveries by reducing the time spent on reproducing
experiments.
- Measuring the impact of papers beyond citations.
- Overcoming the replication crisis and fight against research misconducts (eg: p-
hacking).
35. Open Science
Moabb (Mother of All Brain-Computer Interface Benchmarks):
Open Science Project: (Work in progress)
- BCI Benchmarking tool: Compare
different BCI algorithms for different
EEG Paradigms on different datasets.
- Created by NeuroTechX community.
- Based on Python Open Source
packages: MNE, Scikit-learn, ...