This document provides information on preparing data for sharing and publishing. It discusses organizing data through clear file and folder labeling, including additional context about methods and instruments. It also describes publishing data through journals like Scientific Data, which provide peer review and credit. Sensitive data requires careful handling and may be suitable for controlled access repositories. Overall the document offers guidance on effective data organization, documentation, sharing and receiving credit for shared data.
Lesson 8 in a set of 10 created by DataONE on Best Practices for 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.
Long-term storage – will it fill up with the good stuff, or the big, bad, an...DCC-info
Presentation by Angus Whyte at DCC-Arkivum event 'Data Storage & Preservation Strategies for Research Data Management' at University of Edinburgh 27 October 2014
Lesson 8 in a set of 10 created by DataONE on Best Practices for 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.
Long-term storage – will it fill up with the good stuff, or the big, bad, an...DCC-info
Presentation by Angus Whyte at DCC-Arkivum event 'Data Storage & Preservation Strategies for Research Data Management' at University of Edinburgh 27 October 2014
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.
FAIR Data Knowledge Graphs–from Theory to PracticeTom Plasterer
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen. Our processes enable simple creation of dataset records and linking to source data, providing a seamless federated knowledge graph for novice and advanced users alike.
Presented May 7th, 2019 at the Knowledge Graph Conference, Columbia University.
Presentation on data sharing that outlines five layers that must be addressed to enable data to be located, obtained, access, understood and use, and cited.
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 7 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.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
Data Publishing at Harvard's Research Data Access SymposiumMerce Crosas
Data Publishing: The research community needs reliable, standard ways to make the data produced by scientific research available to the community, while giving credit to data authors. As a result, a new form of scholarly publication is emerging: data publishing. Data publishing - or making data reusable, citable, and accessible for long periods - is more than simply providing a link to a data file or posting the data to the researcher’s web site. We will discuss best practices, including the use of persistent identifiers and full data citations, the importance of metadata, the choice between public data and restricted data with terms of use, the workflows for collaboration and review before data release, and the role of trusted archival repositories. The Harvard Dataverse repository (and the Dataverse open-source software) provides a solution for data publishing, making it easy for researchers to follow these best practices, while satisfying data management requirements and incentivizing the sharing of research data.
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.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
This slideshow was used at a lunchtime session delivered at the Humanities Division, University of Oxford, on 2014-05-12. It provides a general overview of some key data management topics, plus some pointers on where to find further information.
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
A talk outlining the virtues and processes of Research Data Management for PhD students in the geosciences. Given by Stuart Macdonald at the Introduction to RDM Workshop, School of Geosciences, University of Edinburgh, on 2 November 2015
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.
FAIR Data Knowledge Graphs–from Theory to PracticeTom Plasterer
FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world’s data. We started with data catalogues (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen. Our processes enable simple creation of dataset records and linking to source data, providing a seamless federated knowledge graph for novice and advanced users alike.
Presented May 7th, 2019 at the Knowledge Graph Conference, Columbia University.
Presentation on data sharing that outlines five layers that must be addressed to enable data to be located, obtained, access, understood and use, and cited.
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 7 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.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
Data Publishing at Harvard's Research Data Access SymposiumMerce Crosas
Data Publishing: The research community needs reliable, standard ways to make the data produced by scientific research available to the community, while giving credit to data authors. As a result, a new form of scholarly publication is emerging: data publishing. Data publishing - or making data reusable, citable, and accessible for long periods - is more than simply providing a link to a data file or posting the data to the researcher’s web site. We will discuss best practices, including the use of persistent identifiers and full data citations, the importance of metadata, the choice between public data and restricted data with terms of use, the workflows for collaboration and review before data release, and the role of trusted archival repositories. The Harvard Dataverse repository (and the Dataverse open-source software) provides a solution for data publishing, making it easy for researchers to follow these best practices, while satisfying data management requirements and incentivizing the sharing of research data.
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.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
Dataset Catalogs as a Foundation for FAIR* DataTom Plasterer
BioPharma and the broader research community is faced with the challenge of simply finding the appropriate internal and external datasets for downstream analytics, knowledge-generation and collaboration. With datasets as the core asset, we wanted to promote both human and machine exploitability, using web-centric data cataloguing principles as described in the W3C Data on the Web Best Practices. To do so, we adopted DCAT (Data CATalog Vocabulary) and VoID (Vocabulary of Interlinked Datasets) for both RDF and non-RDF datasets at summary, version and distribution levels. Further, we’ve described datasets using a limited set of well-vetted public vocabularies, focused on cross-omics analytes and clinical features of the catalogued datasets.
This slideshow was used at a lunchtime session delivered at the Humanities Division, University of Oxford, on 2014-05-12. It provides a general overview of some key data management topics, plus some pointers on where to find further information.
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
A talk outlining the virtues and processes of Research Data Management for PhD students in the geosciences. Given by Stuart Macdonald at the Introduction to RDM Workshop, School of Geosciences, University of Edinburgh, on 2 November 2015
Presentation to IASSIST 2013, in the session Expanding Scholarship: Research Journals and Data Linkages. Describes PREPARDE workshop on repository accreditation for data publication and invites comments on guidelines.
Introduction to research data managementdri_ireland
An Introduction to Research Data Management: slides from a presentation given online on May 12 2022, by Beth Knazook, Project Manager, Research Data. Covers topics such as: what are research data; why share research data; why DMPs are important; and where should you share your data?
PIDs, Data and Software: How Libraries Can Support Researchers in an Evolving...Sarah Anna Stewart
Presentation given at the M25 Consortium of Academic Libraries, CPD25 Event on 'The Role of the Library in Supporting Research'. Provides an introduction to data, software and PIDs and a brief look at how libraries can enable researchers to gain impact and credit for their research data and software.
Slides from Thursday 2nd August 2018 - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Presentation by Ruth Wilson on Nature Publishing Group's Scientific Data journal given at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Lessons from the UK: Data access, patient trust & real-world impact with heal...Varsha Khodiyar
Slides supporting presentation given at the virtual Beilstein Open Science Symposium in October 2021.
Abstract:
Health Data Research UK’s mission is to unite the UK’s health data to enable discoveries that improve people’s lives. Our 20-year vision is for large scale data and advanced analytics to benefit every patient interaction, clinical trial, biomedical discovery and enhance public health. A key part of HDR UK’s vision is our data portal, the Innovation Gateway. The Gateway facilitates discovery of healthcare data and simplifies data request procedures across multiple data custodians. The Gateway contains metadata on a variety of datasets, including those related to COVID-19, cardiovascular, maternal health, emergency care, primary care, secondary care, acute care, palliative care, biobanks, research cohorts and deeply phenotyped patient cohorts.
From the outset HDR UK has sought the voices, views and experiences of patient and lay-public groups to ensure there is transparency and clear public benefit in the use of the UK’s health data. Patient and public involvement is key to making the Gateway accessible, transparent and to ensure public confidence in research access to health data. The importance of public outreach combined with providing research access to data is illustrated with HDR UK’s contribution to the UK’s coronavirus pandemic response. HDR UK was tasked by the UK’s Chief Scientific Office to build and facilitate the infrastructure to support the National Core Studies, providing key insights on the evolving situation to UK policy makers during the course of the pandemic.
In this talk, I will show how HDR UK is enabling open science by facilitating the discovery of health data, and simplifying the process of requesting access to multiple datasets. I’ll discuss HDR UK’s approach to embedding transparency on research data usage for patients and public, and summarise some of the key ways in which HDR UK has contributed to the coronavirus pandemic.
The information in this slide deck was presented at the Covid Crisis in India - Information & Appeal on Sunday 23rd May 2021.
If you find the information in this slide deck useful, please donate to https://justgiving.com/fundraising/covidcrisisinindia
Data citation and sharing during article publicationVarsha Khodiyar
Deck presented to CHORUS forum on 21st Jan 2021, as part of panel on Data Citations & Sharing (https://www.chorusaccess.org/events/chorus-forum-new-connections/)
What role can publishers play in the open data ecosystem?Varsha Khodiyar
Presentation at session 3 of the NIH workshop 'Role of Generalist Repositories to Enhance Data Discoverability and Reuse' on Feb 11th, at the NIH Main Campus.
New approaches to data management: supporting FAIR data sharing at Springer N...Varsha Khodiyar
Presentation given at Biocuration 2019 Session 5 (Data standards and ontologies: Making data FAIR)
Abstract:
Since 2016, academic publishers including Springer Nature, Elsevier and Taylor & Francis have been providing standard research data policies to journal authors, reflecting key aspects of the FAIR Principles’ practical applications: sharing data in repositories, using persistent identifiers and citing data appropriately. In spite of the rise of FAIR and good data management practice, recent surveys found that nearly 60% of researchers had never heard of the FAIR Principles, and 46% are not sure how to organise their data in a presentable and useful way. In this presentation we will analyse the results of a white paper which assessed the key challenges faced by researchers in sharing their data, and discuss current initiatives and approaches to support researchers to adopt good data sharing practice.
These include the roll-out of research data policies since 2016, as well as the launch of a Helpdesk service which has provided support to authors and allowed the research data team to capture more granular information on the challenges they face in sharing their data. We will also discuss the development of a third-party curation service which assists authors in depositing their data into appropriate repositories, and drafting data availability statements.
Finally we will assess the impacts of some of these interventions, including an analysis of data availability statements and an overview of the methods authors are currently using to share their data, and how these align with FAIR.
The value of data curation as part of the publishing processVarsha Khodiyar
Presentation given at Biocuration 2019 Session 5 (Interacting with the Research Community)
Abstract:Journals and publishers have an important role to play in the drive to increase the reproducibility of published science. Since its launch in 2014, the Nature Research journal Scientific Data has established a reputation for publishing data papers (‘Data Descriptors’) that are highly reusable, as evidenced by a strong citation record. One of the ways in which Scientific Data ensures maximum reusability of published data is via the in-house data curation workflow applied to every Data Descriptor. In 2017, Springer Nature launched its Research Data Support (RDS) service to provide data curation expertise to researchers publishing at other Springer Nature journals.
During curation at Scientific Data and RDS, our data editors familiarise themselves with the related manuscript and perform a thorough check of each data archive. This ensures the descriptions in the manuscript match the metadata and data at the data repositories. The curation process facilitates the identification of any discrepancies between the manuscript text and the information held at the data repository.
Over the last year, the curation team have been recording the types of discrepancies rectified as a direct result of our curation process. At Scientific Data approximately 10% of the discrepancies the team find are significant enough to potentially have warranted a formal correction had the issue had not been resolved prior to publication.
In this presentation we give an overview of our observed outcomes from embedding data curation within the publishing process. We describe of how we are monitoring the value of our curation work, and show examples of the types of discrepancy most commonly identified through curation at Scientific Data and RDS.
Facilitating good research data management practice as part of scholarly publ...Varsha Khodiyar
Presentation given to the SciDataCon #IDW2018 session: Democratising Data Publishing: A Global Perspective, on Tuesday 6th November 2018, Gaborone, Botswana
Practical challenges for researchers in data sharingVarsha Khodiyar
Presentation given at the Research Data Alliance Plenary 12 session: IG Open Questionnaire for Research Data Sharing Survey, on Tuesday 6th November 2018, Gaborone, Botswana
Update from Data policy standardisation and implementation IGVarsha Khodiyar
Update given to the Research Data Alliance Plenary 12 joint meeting session: WG FAIRSharing Registry and Data Policy Standardisation and Implementation IG, on Monday 5th November 2018, Gaborone, Botswana
Data Publishing and Institutional RepositoriesVarsha Khodiyar
Slides presented at the Force16 panel discussion on 18th April 2016 "Libraries united in opening new scholarly platforms" https://www.force11.org/meetings/force2016/program/agenda/concurrent-session-libraries-united-opening-new-scholarly
Richard's entangled aventures in wonderlandRichard 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.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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.
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.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
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.
Nutraceutical market, scope and growth: Herbal drug technology
Preparing your data for sharing and publishing
1. Preparing your data for sharing
and publishing
Varsha Khodiyar, PhD
MRC Cognition and Brain Sciences Unit
Open Science Day 20.11.2018
Howchameleonschangecolour
2. 1
7719 respondents
White paper available from
https://doi.org/10.6084/m9.figshare.5975011
Survey data available from
https://doi.org/10.6084/m9.figshare.5971387
What are researchers concerned about when sharing data?
6. 5
Make sure your data are well organised
• Data files and folders labelled in an understandable way.
• Data files and folders organised in a logical, easy-to-
follow manner.
• Any acronyms used for data file/folder names clearly
defined, ideally in a README file.
• Data files in a format that are easy for others to reuse /
use the standard format used by your discipline.
7. 6
Increasing reproducibility
• Include any additional information needed to understand the data,
methods, parameters, e.g. which instrument (make and model) was
used to measure blood carbon dioxide levels?
• Include availability statements for any code that was used to view,
parse or analyse the data, in support of the conclusions.
8. 7
Help to organise your data is available
Springer Nature Research Data Support
Researchers
submit their
data files
securely
The Research
Data team
curates the data
and metadata
The data are
published and
linked to the
author’s paper
More information is available on our website here:
http://www.springernature.com/gb/group/data-
policy/data-support-services
9. 8
No one other than the
creator can access the
data, or even knows that
it exists
Before data curation: a researcher’s dataset in a desktop
folder
The dataset is
stored as an
Excel file in a
desktop folder
The file title is not
comprehensible to
anyone but the
creator
No description or
keywords
available
10. 9
Before curation begins
Once received, we check to make sure
that the dataset is suitable for our
curation services. Multiple files in any
format are accepted.
After making these checks, we begin
the curation process. If necessary
we may recommend that the
dataset is split into smaller groups
or collections.
Pre-curation data checks:
The data aren’t sensitive
The data don’t include
direct or indirect human
identifiers
The data shouldn’t be in a
community repository
11. 10
After Springer Nature Research Data Support
Working with the researcher’s manuscript or published paper, we draft a comprehensive
metadata record for the dataset which is sent to the researcher for approval before
being published. Embargoes can be applied if necessary.
The curated dataset will be published with
its own metadata record which includes
rich descriptive information, reuse
conditions, licence, DOI, metrics and
keywords
(this example is
https://doi.org/10.6084/m9.figshare.5259
415)
13. 12
Selecting a repository for your data
Considerations:
1. Is there a discipline-specific repository for the type of data you
have generated?
2. Will access to the data need to be controlled?
3. If no discipline-specific repository is available for the data,
does your funder or institute mandate deposition to a
particular repository?
14. 13
Indexing services Curated lists
Sources to help choose a data repository
NEW! Tools to help select repositories
www.nature.com/sdata/data-policies/repositories
https://repositoryfinder.test.datacite.org/
16. 15
• Consider an appropriate patient consent
framework
Consent to use data in current study
Consent to use data for future research
Consent to share data for use by other
research groups
• Don’t collect more than you need
Collecting sensitive data
17. 16
• Remove direct identifiers
• Aggregate indirect identifiers into groups where possible
• Anonymization or de-identification?
• Use controlled access repositories,
and consider:
Data use agreement?
Data access conditions?
Sharing sensitive data
19. 18
Data Journals at Springer Nature
www.nature.com/scientificdata
https://bmcresnotes.biomedcentral.com
Data Descriptor
Open access
Sound science
Emphasis on enabling
data reuse
Data peer review
Data Note
Open access
Sound science
Short format
20. 19
Scientific Data, a Nature Research journal
Data Descriptor
Primary article type; sound
science and facilitates data
reuse
Analysis
New analyses or meta-
analyses of existing data
Article
Original reports on
advances in data sharing &
reuse
Comment
Announcements of broad
interest; usually invited
www.nature.com/scientificdata
21. 20
Under the hood of a Data Descriptor
• Context for data generation (background)
• How was data generated?
• How was data processed?
• Where is the data?
• Synthesis
• Analysis
• Conclusions
22. 21
Data peer review
www.nature.com/sdata/policies/for-referees
Experimental
Rigor and
Technical Data
Quality
Were data produced in a sound manner?
Technical quality of data – appropriate statistical analyses?
Experimental rigor - appropriate depth, coverage?
Completeness
of the
Description
Sufficient detail to allow others to reproduce these steps?
Sufficient detail to allow others to reuse this data?
Consistent with relevant minimum reporting standards?
Integrity of the
Data Files and
Repository
Record
Do data files appear complete and match manuscript
descriptions?
Are data archived to the most appropriate repository?
23. 22
What types of data can be published?
Decades old
dataset
Standalone
dataset
Data that has been
used in an analysis
article
Large
consortium
dataset
Data from a
single
experiment
Any data that the researcher
finds valuable and that others
might find useful too
Data associated with a
high impact analysis
article
24. 23
When can a data paper be published?
After data
analysis has been
published
Before analysis has
been published
Authors not
intending to
analyse data
Data papers can be
submitted and published at
any point in the research
workflow, i.e. whenever it
makes most sense for your
data
After data
analysis has been
published
Before the
analysis has been
published
Publication alongside
analysis article
26. 25
What does research data training offer?
• Directly addresses the main challenges of data sharing
• Part of the Nature Research Academies, offering trusted quality
and value
• A unique perspective and trusted experience within the realm of
research data
• Training for both researchers & information professionals,
appropriate for all levels
• Courses are customised to meet your needs, and are brought to
you (and your researchers)
Springer Nature Research Data Training
Source: https://doi.org/10.6084/m9.figshare.5975011
27. 26
Queries are answered within two business days
Run by members of the Springer Nature Research Data team
Expertise in data curation and management, archiving and
digital preservation, copyright and licensing, Open Access
publishing
Always encourage best practices, e.g. the use of community
repositories for specific data types
Email: researchdata@springernature.com
http://www.springernature.com/gp/group/data-policy/helpdesk
Springer Nature Research Data Helpdesk
28. 2727
The story behind the image
How chameleons change colour
Chameleons are well known for their potential to
change colour but recent research on panther
chameleons is the first to find two layers of
crystal containing cells, each with a potentially
different purpose. Researchers from the
University of Geneva have speculated that the
deeper crystal containing cells may help with the
regulation of temperature, whilst the more
superficial layer of colour changing cells could be
responsible for camouflage or mating displays.
Thank you for listening
Varsha Khodiyar, PhD
Data Curation Manager, Springer Nature
(Data Curation Editor, Scientific Data)
varsha.khodiyar@nature.com