Data Repositories: Recommendation, Certification and Models for Cost RecoveryAnita de Waard
Talk at NITRD Workshop "Measuring the Impact of Digital Repositories" February 28 – March 1, 2017 https://www.nitrd.gov/nitrdgroups/index.php?title=DigitalRepositories
Funders and publishers have something in common: for better or worse, we have the ability to influence the behavior of researchers. This talk will focus on what both groups can do to improve research now and in the future.
Data Repositories: Recommendation, Certification and Models for Cost RecoveryAnita de Waard
Talk at NITRD Workshop "Measuring the Impact of Digital Repositories" February 28 – March 1, 2017 https://www.nitrd.gov/nitrdgroups/index.php?title=DigitalRepositories
Funders and publishers have something in common: for better or worse, we have the ability to influence the behavior of researchers. This talk will focus on what both groups can do to improve research now and in the future.
December 9, 2015 NISO Webinar: Two-Part Webinar: Emerging Resource Types - Pa...DeVonne Parks, CEM
Addressing the New Challenges in Data Sharing: Large-Scale Data and Sensitive Data
Mercè Crosas, Chief Data Science and Technology Officer, IQSS, Harvard University
An update on the latest BioSharing work; including work with ELIXIR and NIH BD2K, also our survey to assess user needs (530 replies) and the work on the recommender tool
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 presentation was given by Jon Wheeler and Karl Benedict of the University of New Mexico during the joint NISO-NFAIS Virtual Conference held on December 7, 2016
February 18 2015 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Network Effects: RMap Project
Sheila M. Morrissey, Senior Researcher, ITHAKA
This presentation was provided by Karen Baker, University of Illinois - Urbana-Champaign, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
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.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This talk presents a set of detailed technical recommendations for operationalizing the Joint Declaration of Data Citation Principles (JDDCP) - the most widely agreed set of principle-based recommendations for direct scholarly data citation.
We will provide initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data.
We hope that these recommendations along with the new NISO JATS document schema revision, developed in parallel, will help accelerate the wide adoption of data citation in scholarly literature. We believe their adoption will enable open data transparency for validation, reuse and extension of scientific results; and will significantly counteract the problem of false positives in the literature.
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
December 9, 2015 NISO Webinar: Two-Part Webinar: Emerging Resource Types - Pa...DeVonne Parks, CEM
Addressing the New Challenges in Data Sharing: Large-Scale Data and Sensitive Data
Mercè Crosas, Chief Data Science and Technology Officer, IQSS, Harvard University
An update on the latest BioSharing work; including work with ELIXIR and NIH BD2K, also our survey to assess user needs (530 replies) and the work on the recommender tool
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 presentation was given by Jon Wheeler and Karl Benedict of the University of New Mexico during the joint NISO-NFAIS Virtual Conference held on December 7, 2016
February 18 2015 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Network Effects: RMap Project
Sheila M. Morrissey, Senior Researcher, ITHAKA
This presentation was provided by Karen Baker, University of Illinois - Urbana-Champaign, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
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.
Data Citation Implementation Guidelines By Tim Clarkdatascienceiqss
This talk presents a set of detailed technical recommendations for operationalizing the Joint Declaration of Data Citation Principles (JDDCP) - the most widely agreed set of principle-based recommendations for direct scholarly data citation.
We will provide initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data.
We hope that these recommendations along with the new NISO JATS document schema revision, developed in parallel, will help accelerate the wide adoption of data citation in scholarly literature. We believe their adoption will enable open data transparency for validation, reuse and extension of scientific results; and will significantly counteract the problem of false positives in the literature.
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
Nothing can be added to you, nor anything taken from youdonmardak
Nothing can be added to you, nor anything taken from you. To find more daily insights, please visit DonMardak.com
Don Mardak
Insights
Meditation
Spirituality
Quotes
Artículos del Estatuto del Profesor Universitario donde se hace mención del régimen de jubilaciones y pensiones del personal docente de la Universidad de Carabobo (artículos 321 al 328)
Uso del Whatsapp en las empresas. Recomendaciones para un uso eficiente y que cuide las relaciones de las personas. Whatsapp como herramienta formal o informal de comunicación interna.
PAARL's 1st Marina G. Dayrit Lecture Series held at UP's Melchor Hall, 5F, Proctor & Gamble Audiovisual Hall, College of Engineering, on 3 March 2017, with Albert Anthony D. Gavino of Smart Communications Inc. as resource speaker on the topic "Using Big Data to Enhance Library Services"
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent 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, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. 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 and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
an introductory course for Librarians on using Big Data and Data Science applications on the field of Library Science. The course is a 2 hour course module for basic fundamentals of applying DS work.
This presentation was provided by Chris Erdmann of Library Carpentries and by Judy Ruttenberg of ARL during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble
Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032
A presentation I gave at the 2018 Molecular Med Tri-Con in San Francisco, February 2018. This addresses the general challenge of biomedical data management, some of the things to consider when evaluation solutions in this space, and concludes with a brief summary of some of the tools and platforms in this space.
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
Responsible conduct of research: Data ManagementC. Tobin Magle
A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
Similar to Real-World Data Challenges: Moving Towards Richer Data Ecosystems (20)
Talk at the World Science Festival at Columbia, June 2, 2017: session on Big Data and Physics: http://www.worldsciencefestival.com/programs/big-data-future-physics/
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
insect taxonomy importance systematics and classification
Real-World Data Challenges: Moving Towards Richer Data Ecosystems
1. | 1
Anita de Waard 0000-0002-9034-4119
VP Research Data Collaborations
Elsevier RDM Services
a.dewaard@elsevier.com
Big Data PI Meeting
March 16, 2016
Real-World Data
Challenges:
Moving Towards
Richer Data Ecosystems
4. | 4
Trend # 3: Computers are scientists, too!
“intelligent systems for computer-aided
discovery can complement and integrate
into the insight generation loop in
scalable ways…”
http://ieeexplore.ieee.org/abstract/document/7515118/: Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support
“This work combines time series Principal
Component Analysis with InSAR to constrain
the space of possible model explanations on
current empirical data sets and achieve a better
identification of deformation patterns”
5. | 5
Raising many technical/organisational/policy questions:
• Is Long-Tail Data + Semantics = Big Data?
• Is Data Science a field, or a skill? (A department, or a class?)
• Are supercomputing centers research departments or bits of infrastructure? (And if
infrastructure, are they part of IT? (“Oh, no, anything but that!”)
• Are repositories places to store outputs, or places where science is conducted?
• If so, how are repositories and HPC’s recognised and rewarded?
• How can we keep track of (micro)provenance of parts of data sets?
• Should we explore Blockchain technology for this? (“Oh no, anything but that!”)
• Is a piece of software part of the University’s Research Outputs?
• If so, how do we reward brilliant coders who blog, but don’t write?
• How do we reward (virtual) collaboration?
• Why won’t those damn scientists share their data?
• Who will own the Data Science Cloud: Amazon? Or the joint HPC’s (NDS??) Is NIH
Data Commons the Model? Or is this a free for all? What is the role of commercial
parties?
• Is data curation/stewardship a part of science, or a glorified administrator's job?
• What is the role of libraries, in all this?
• And why the hell is a publisher talking about it?
6. | 6 6
Inst. Data
Repositorie(s)
Lab
ELN(s)
Data
Journal
Data search
Link to article
Journal
Find
Topic
Identify
gaps
Plan &
Fund
Discover data, people,
methods & protocols
Collect, analyze &
vizualize
Store, preserve
& share
Publish
Prepare, reproduce,
re-use & benchmark
Domain-specific
Repositories
General search
Faculty
LIMS
Data
center
Inst. Data
Repositorie(s)
Lab
ELN(s)
Data
Journal
Data search
Data Management
Plans
Metadata, methods &
protocols ready for
preservation and publishing
Link to article
Journal
Publish data
(under embargo)
Secure
discoverability
in & outside
the institution
Plan each step from
experiment to publish
Domain-specific
Repositories
General search
What Elsevier is Interested in: Supporting RDM Networks
7. | 7
Biological Pathways extracted via
semantic text mining
A upregulates B
B upregulates C
C increases disease D
Normalizing vocabularies required: proteins, diseases, drugs, chemicals
A B C D
Bioactivities
through text analysis
IC50 6.3nM, kinase binding assay
10mM concentration
Chemical Structures
And Properties
InChi,
Name
NCBI,
Uniprot
EMTREE
ReaxysTree,
Structures
What Elsevier is Interested in: Knowledge Graphs in Life
Science