DataTags, The Tags Toolset, and Dataverse IntegrationMichael Bar-Sinai
This presentation describes the concept of DataTags, which simplifies handling of sensitive datasets. It then shows the Tags toolset, and how it is integrated with Dataverse, Harvard's popular dataset repository.
2017 05 03 Implementing Pure at UWA - ANDS Webinar SeriesKatina Toufexis
The UWA Library has recently implemented the Current Research Information System – Elsevier’s Pure as our Research Repository.
This is a researcher profiling system which allows us to link publications, theses and grants to our researchers.
We are also managing another separate repository which holds our research datasets which uses the DSpace platform. This is called Research Data Online.
In order to consolidate our systems and resolve ongoing issues which we have with our highly customised version of DSPace, we have embarked on migrating our current datasets from Dspace into Pure.
We have encountered a few hurdles:
-We need to manually migrate our current datasets from DSpace to Pure
-We needed to create a crosswalk from Pure to ANDS’ Research Data Australia in order to harvest our datasets
We cannot automatically mint DOIs from within Pure and thus have need to change our administrator validation workflows to include a manual DOI minting step.
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.
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
DataTags, The Tags Toolset, and Dataverse IntegrationMichael Bar-Sinai
This presentation describes the concept of DataTags, which simplifies handling of sensitive datasets. It then shows the Tags toolset, and how it is integrated with Dataverse, Harvard's popular dataset repository.
2017 05 03 Implementing Pure at UWA - ANDS Webinar SeriesKatina Toufexis
The UWA Library has recently implemented the Current Research Information System – Elsevier’s Pure as our Research Repository.
This is a researcher profiling system which allows us to link publications, theses and grants to our researchers.
We are also managing another separate repository which holds our research datasets which uses the DSpace platform. This is called Research Data Online.
In order to consolidate our systems and resolve ongoing issues which we have with our highly customised version of DSPace, we have embarked on migrating our current datasets from Dspace into Pure.
We have encountered a few hurdles:
-We need to manually migrate our current datasets from DSpace to Pure
-We needed to create a crosswalk from Pure to ANDS’ Research Data Australia in order to harvest our datasets
We cannot automatically mint DOIs from within Pure and thus have need to change our administrator validation workflows to include a manual DOI minting step.
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.
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
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.
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
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Preparing your data for sharing and publishingVarsha Khodiyar
Talk given as part of the MRC Cognition and Brain Sciences Unit Open Science Day on 20th November 2018 , University of Cambridge (https://www.eventbrite.co.uk/e/open-science-day-at-the-mrc-cbu-tickets-50363553745)
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.
DataTags: Sharing Privacy Sensitive Data by Michael Bar-sinaidatascienceiqss
The DataTags framework makes it easy for data producers to deposit, data publishers to store and distribute, and data users to access and use datasets containing confidential information, in a standardized and responsible way. The talk will first introduce the concepts and tools behind DataTags, and then focus on the user-facing component of the system - Tagging Server (available today at datatags.org). We will conclude by describing how future versions of Dataverse will use DataTags to automatically handle sensitive datasets, that can only be shared under some restrictions.
BROWN BAG TALK WITH MICAH ALTMAN INTEGRATING OPEN DATA INTO OPEN ACCESS JOURNALSMicah Altman
This talk, is part of the MIT Program on Information Science brown bag series (http://informatics.mit.edu)
This talk discusses findings from an analysis of data sharing and citation policies in Open Access journals and describes a set of novel tools for open data publication in open access journal workflows. Bring your lunch and enjoy a discussion fit for scholars, Open Access fans, and students alike.
Dr Micah Altman is Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, at the Massachusetts Institute of Technology.
Case Study Big Data: Socio-Technical Issues of HathiTrust Digital TextsBeth Plale
Invited talk at TRUST Women’s Institute for Summer Enrichment (WISE), Cornell, NY Jun 16, 2014. Infrastructure support for text mining research of big data repository like HathiTrust raises challenges in access and security when the bulk of the repository is protected by copyright.
This presentation was provided by Tim McGeary of Duke University during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
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.
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
Aim:- To show how research data management can contribute to the success of your PhD.
*What is research data and why it is important?
*The Research Data lifecycle
* Research Data – more than just your results
* FAIR data and Open Research
* DMP online tool
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.
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
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Preparing your data for sharing and publishingVarsha Khodiyar
Talk given as part of the MRC Cognition and Brain Sciences Unit Open Science Day on 20th November 2018 , University of Cambridge (https://www.eventbrite.co.uk/e/open-science-day-at-the-mrc-cbu-tickets-50363553745)
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.
DataTags: Sharing Privacy Sensitive Data by Michael Bar-sinaidatascienceiqss
The DataTags framework makes it easy for data producers to deposit, data publishers to store and distribute, and data users to access and use datasets containing confidential information, in a standardized and responsible way. The talk will first introduce the concepts and tools behind DataTags, and then focus on the user-facing component of the system - Tagging Server (available today at datatags.org). We will conclude by describing how future versions of Dataverse will use DataTags to automatically handle sensitive datasets, that can only be shared under some restrictions.
BROWN BAG TALK WITH MICAH ALTMAN INTEGRATING OPEN DATA INTO OPEN ACCESS JOURNALSMicah Altman
This talk, is part of the MIT Program on Information Science brown bag series (http://informatics.mit.edu)
This talk discusses findings from an analysis of data sharing and citation policies in Open Access journals and describes a set of novel tools for open data publication in open access journal workflows. Bring your lunch and enjoy a discussion fit for scholars, Open Access fans, and students alike.
Dr Micah Altman is Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, at the Massachusetts Institute of Technology.
Case Study Big Data: Socio-Technical Issues of HathiTrust Digital TextsBeth Plale
Invited talk at TRUST Women’s Institute for Summer Enrichment (WISE), Cornell, NY Jun 16, 2014. Infrastructure support for text mining research of big data repository like HathiTrust raises challenges in access and security when the bulk of the repository is protected by copyright.
This presentation was provided by Tim McGeary of Duke University during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
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.
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
Aim:- To show how research data management can contribute to the success of your PhD.
*What is research data and why it is important?
*The Research Data lifecycle
* Research Data – more than just your results
* FAIR data and Open Research
* DMP online tool
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.
At Utah State University, a pilot project is under development to evaluate the benefits of tracking data sets and faculty publications using the online catalog and the Library’s institutional repository.
With federal mandates to make publications and data open, universities look for solutions to track compliance. At Utah State University, the Sponsored Programs Office follows up with researchers to determine where data has been or will be deposited, per the terms of their grant.
Interested in making this publicly discoverable, the Library, Sponsored Programs, and Research Office are working together to pilot a project that enables the creation of publicly accessible MARC and Dublin Core records for data deposited by USU faculty. This project aims to make data sets, as well as publications, visible in research portals such as WorldCat, as well through Google searches.
This presentation will describe the project and anticipated benefits, as well as outline the roles of the cataloging staff and data librarian, and the involvement of the Research Office.
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 given at the Indiana University School of Medicine's Ruth Lilly Medical Library. Contains information and resources specific to Indiana University Purdue University Indianapolis (IUPUI). For full class materials, see LYD17_IUPUIWorkshop folder here: https://osf.io/r8tht/.
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.
IDW2022: A decades experiences in transparent and interactive publication of ...GigaScience, BGI Hong Kong
Scott Edmunds at International Data Week 2022: A decades experiences in transparent and interactive publication of FAIR data and software via an end-to-end XML publishing platform. 21st June 2022
GigaByte Chief Editor Scott Edmunds presents on how to prepare a data paper for the TDR and WHO sponsored call for data papers describing datasets on vectors of human diseases launched in Nov 2021. Presented at the GBIF webinar on 25th January 2022 and aimed at authors interested in submitting a manuscript submitted to the series.
STM Week: Demonstrating bringing publications to life via an End-to-end XML p...GigaScience, BGI Hong Kong
Scott Edmunds at the STM Week 2020 Digital Publishing seminar on Demonstrating bringing publications to life via an End-to-end XML publishing platform. 2nd December 2020
Scott Edmunds: A new publishing workflow for rapid dissemination of genomes u...GigaScience, BGI Hong Kong
Scott Edmunds on a new publishing workflow for rapid dissemination of genomes using GigaByte & GigaDB. Presented at Biodiversity 2020 in the Annotation & Databases track, 9th October 2020.
Scott Edmunds: Quantifying how FAIR is Hong Kong: The Hong Kong Shareability ...GigaScience, BGI Hong Kong
Scot Edmunds talk at CODATA2019 on Quantifying how FAIR is Hong Kong: The Hong Kong Shareability of Hong Kong University Research Experiment. 19th September 2019 in Beijing
Scott Edmunds talk at IARC: How can we make science more trustworthy and FAIR...GigaScience, BGI Hong Kong
Scott Edmunds talk at IARC, Lyon. How can we make science more trustworthy and FAIR? Principled publishing for more evidence based research. 8th July 2019
PAGAsia19 - The Digitalization of Ruili Botanical Garden Project: Production...GigaScience, BGI Hong Kong
A 3 part talk presented at PAG Asia 2019 in Shenzhen- The Digitalization of Ruili Botanical Garden Project: Production, Curation and Re-Use. Presented by Huan Liu (CNGB), Scott Edmunds (GigaScience) & Stephen Tsui (CUHK). 8th June 2019
Democratising biodiversity and genomics research: open and citizen science to...GigaScience, BGI Hong Kong
Scott Edmunds at the China National GeneBank Youth Biodiversity MegaData Forum: Democratising biodiversity and genomics research: open and citizen science to build trust and fill the data gaps. 18th December 2018
Ricardo Wurmus at #ICG13: Reproducible genomics analysis pipelines with GNU Guix. Presented at the GigaScience Prize Track at the International Conference on Genomics, Shezhen 26th October 2018
Paul Pavlidis at #ICG13: Monitoring changes in the Gene Ontology and their im...GigaScience, BGI Hong Kong
Paul Pavlidis talk at the #ICG13 GigaScience Prize Track: Monitoring changes in the Gene Ontology and their impact on genomic data analysis (GOtrack). Shenzhen, 26th October 2018
Stefan Prost at #ICG13: Genome analyses show strong selection on coloration, ...GigaScience, BGI Hong Kong
Stefan Prost presentation for the #ICG13 GigaScience Prize Track: Genome analyses show strong selection on coloration, morphological and behavioral phenotypes in birds-of-paradise. Shenzhen, 26th October, 2018
Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 67...GigaScience, BGI Hong Kong
Lisa Johnson's talk at the #ICG13 GigaScience Prize Track: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Shenzhen, 26th October 2018
Reproducible method and benchmarking publishing for the data (and evidence) d...GigaScience, BGI Hong Kong
Scott Edmunds presentation on: Reproducible method and benchmarking publishing for the data (and evidence) driven era. The Silk Road Forensics Conference, Yantai, 18th September 2018
Mary Ann Tuli: What MODs can learn from Journals – a GigaDB curator’s perspec...GigaScience, BGI Hong Kong
Mary Ann Tuli's talk at the International Society of Biocuration meeting : What MODs can learn from Journals – a GigaDB curator’s perspective. Shanghai 9th April 2018
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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.
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.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx
Laurie Goodman at NDIC: Big Data Publishing, Handling & Reuse
1. Big Data Publishing,
Handling, & Reuse
Laurie Goodman, PhD
Editor-in-Chief, GigaScience
Laurie@gigasciencejournal.com
ORCID ID: 0000-0001-9724-5976
Beyond Data Release Mandates
2. What is the point of publishing?
• To disseminate
information/knowledge/ideas.
• To present material so it can be reasonably
assessed for its level of quality (and interest).
• To gain credit for career advancement.
3. What goes into a research article?
+ Area of Interest/
Question
4. What goes into a research article?
+ Area of Interest/
Question
Data & Metadata Collection
Analysis/Hypothesis/Analysis
Conclusions
5. What goes into a research article?
Analysis/Hypothesis/Analysis
Conclusions
+ Area of Interest/
Question
Data & Metadata Collection
6. Scientific Communication
Via Publication
• Scholarly articles are merely advertisement of scholarship .
The actual scholarly artefacts, i.e. the data and
computational methods, which support the
scholarship, remain largely inaccessible --- Jon B.
Buckheit and David L. Donoho, WaveLab and reproducible
research, 1995
• Core scientific statements or assertions are intertwined and
hidden in the conventional scholarly narratives
• Lack of transparency, lack of credit for anything other than
“regular” dead tree publication
7. Kahn, Goodman, & Mittleman. Dragging Scientific Publishing into the 21st Century 2014
http://genomebiology.com/2014/15/12/556
From Journal Delivery to PDF Delivery
8. Lack of Data and Software Availability
Impacts Reproducibility
1. Ioannidis et al., (2009). Repeatability of published microarray gene expression analyses. Nature Genetics 41: 14
2. Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8)
Out of 18 microarray papers, results
from 10 could not be reproduced
9. Retractions are on the Rise
>15X increase in last decade
1. Science publishing: The trouble with retractions http://www.nature.com/news/2011/111005/full/478026a.html
2. Retracted Science and the Retraction Index ▿ http://iai.asm.org/content/79/10/3855.abstract?
10. Deconstructing a paper into accessible,
useable, trackable, interlinked units
Need to provide credit to
reward sharing and proper
organization of:
• Narrative
• Data/Metadata
availability/curation
• Software availability
• Interoperability
• Availability of workflows
• Transparent analyses
Data/
MetaData
Software
Methods
Narrative
11. Deconstructing a paper into accessible,
useable, trackable, interlinked units
Currently we provide credit
for this:
• Narrative
• Data/Metadata
availability/curation
• Software availability
• Interoperability
• Availability of workflows
• Transparent analyses
Data/
MetaData
Software
Methods
Narrative
Sometimes we publish these
as Methods Papers
14. But- publishing ‘Data’ is “Salami Slicing”!!
What is Salami Slicing?
• Publishing research in several different papers that
should form a single cohesive paper
Why is it ‘unethical’
• It fragments the scientific literature, wasting
researcher’s time as they try to get all the information
related to a very specific topic/dataset/method
• It can give the appearance (given there are multiple
publications) that there is large support for a particular
hypothesis
• It pads a researcher’s publication record unfairly
15. Publishing ‘Data’ is “Salami Slicing”!
Baloney
1. Those guidelines were developed prior to the year 2000:
• More than 15 years ago: at a time when data set sizes and data
types collected in the life sciences by a single research group
were relatively small and primarily suitable for a single or narrow
range of disciplines or hypotheses.
• Most journals were not online (which allows easier identification
and access to closely related articles ) until the late ‘90s.
2. In 2005, COPE* ruled that a paper that had data that had been used
and described, at least in part, in a previous publication was not
unethical *Council of Publication Ethics. http://www.publicationethics.org/case/salami-publication
3. Data collection can be (should be!!) a scholarly pursuit:
• Data that is broadly reusable requires care, thought, training,
time, and money to be properly collected, curated, stored, and
shared.
16. Contrary to popular belief…
There are very few
—if any—
‘push-a-button-and-get-it’
reuseable data resources
17. Your not supposed to just collect samples!
*Collect ALL available metadata*
Help Develop a Digital Data Curation Team at your
Institution’s Library (they may already have one…)
18. Back to Darwin
Data & Metadata Collection/Experiments
Analysis/Hypothesis/Analysis
Conclusions
+ Area of Interest/
Question
1839
1859
20 Yrs.
19. Say… was this a Data Publication?
Data & Metadata Collection/Experiments
Analysis/Hypothesis/Analysis
Conclusions
+ Area of Interest/
Question
1839
1859
The most curious fact is the
perfect gradation in the
size of the beaks in the
different species of
Geospiza, from one as large
as that of a hawfinch to
that of a chaffinch, and (if
Mr. Gould is right in including his sub-group, Certhidea, in
the main group) even to that of a warbler. The largest beak
in the genus Geospiza is shown in Fig. 1, and the smallest in
Fig. 3; but instead of there being only one intermediate
species, with a beak of the size shown in Fig. 2, there are no
less than six species with insensibly graduated beaks.
(Chapter 17)
20. DataCite and DOIs
• Aims to “increase acceptance of research data as
legitimate, citable contributions to the scholarly
record”.
• “data generated in the course of research are
just as valuable to the ongoing academic
discourse as papers and monographs”.
Citing Data Isn’t New
The Physical Sciences have been doing this for a while…
21.
22. What we’re doing:
Mandating and Aiding for Data Release
Requiring all data supporting work to be Freely available in a
publically available repository
– How we’re helping to do this:
• Journal-dedicated data and software repository GigaDB
that hosts ALL data types.
• Have Biocurators to aid in handling Metadata
• All Datasets are provided a Digital Object Identifier
(DOI) making them citable and countable
• All Material in GigaDB is available under a CC0 Waiver
• Data with a publically approved database must be
submitted there as well
• Provide Direct links to all associated information
23. Requiring all software and work to be Freely available in a
publically available repository
– How we’re promoting this:
• All software created by authors must be 100% OSI
compliant
• Journal-Dedicated repository GigaDB hosts software so
it can be downloaded.
• Software and Workflows are provided a DOI making
them citable and countable (reward)
• Journal-dedicated Galaxy Platform to run tools
• Have a Data Manager and Data Scientist to wrap and
deploy software tools
• Have our own Github Repository
What we’re doing
Mandating and Aiding Software Release
24. Data Sets in
GigaDB
Analyses/
Workflows in
GigaGalaxy
Paper in
GigaScience
(Narrative + Methods)
Open-access journal Data Publishing Platform
Data Computation Analysis Platform
How we view publishing at GigaScience
25. Making the Data Itself Citable
We provide a linked journal database- this is done to link the data
directly to our papers to ease reproducibility, make it available at the
time of review, and provide authors a place to submit data with no
sustainable ‘home’.
Note: there are many community available databases- so in principle-
any journal can do this by taking advantage of such available
resources.
These include the usual suspects: EBI, NCBI, DDBJ etc.
Databases that take all data types and provide Data DOIs: Dryad,
FigShare, etc.
There are also numerous smaller community databases specific to
different fields or data types.
26. Some of the Journals Currently
Doing Data Publication
http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList
27. Citing Data in the
References Allows Tracking
This rewards authors for making data
available AND makes it easier to find
But is this being done?
33. Improving Quality as
Well as Availability
How Hard is Data and Software Review?
Not really that much harder than narrative
review.
34. Fail – submitter is
provided error report
Pass – dataset is
uploaded to
GigaDB.
Curator makes dataset public
(can be set as future date if
required)
DataCite
XML file
Submission
Submitter logs in to
GigaDB website and
uploads Excel submission
or uses online wizard
DOI
assigned
Files
Submitter provides
files by ftp or
Aspera
XML is generated and
registered with DataCite
Curator Review
Curator contacts submitter with
DOI citation and to arrange file
transfer (and resolve any other
questions/issues).
DOI 10.5524/100003
Genomic data from the
crab-eating
macaque/cynomolgus
monkey (Macaca
fascicularis) (2011)
Public GigaDB dataset
Data must be available for review with the manuscript
(and at the very least get a sanity check…)
35. Reviewing Data in More Detail
Issue: We can’t ask our reviewers to do that!
Our finding: Reviewers don’t mind
Reviewer Dr. Christophe Pouzat on neuroscience
manuscript:
“In addition to making the presented research
trustworthy, the reproducible research
paradigm definitely makes the reviewers job
more fun!”
Can also use specific Data Reviewers (we have)
36. Reviewing DataAND Software
Code in sourceforge under GPLv3:
http://soapdenovo2.sourceforge.net/>5000 downloads
http://homolog.us/wiki/index.php?title=SOAPdenovo2
Data sets
Analyses
Open-Paper
Open-Review
DOI:10.1186/2047-217X-1-18
>35,000 accesses
Open-Code
8 reviewers tested data in ftp server & named reports published
DOI:10.5524/100044
Open-Pipelines
Open-Workflows
DOI:10.5524/100038
Open-Data
78GB CC0 data
Enabled code to being picked apart by bloggers in wiki
37. 8 Reviewers! Holy Cow- that must have
taken forever!!
Submission
July 24
Final review
Aug 28
These were
reviewing
teams from
different labs,
assessing the
materials at
multiple levels
38. Is this really worth the effort?
Beyond Reproducibility:
REUSE
Data Availability and Tools
40. The polar bear DATA were released –prepublication- in 2011
They were used and cited in the following studies- before the main paper on the
sequencing was published
Hailer, F et al., Nuclear genomic sequences reveal that polar bears are an old and distinct
bear lineage. Science. 2012 Apr 20;336(6079):344-7. doi:10.1126/science.1216424.
Cahill, JA et al., Genomic evidence for island population conversion resolves conflicting
theories of polar bear evolution. PLoS Genet. 2013;9(3):e1003345.
doi:10.1371/journal.pgen.1003345.
Morgan, CC et al., Heterogeneous models place the root of the placental mammal
phylogeny. Mol Biol Evol. 2013 Sep;30(9):2145-56. doi:10.1093/molbev/mst117.
Cronin, MA et al., Molecular Phylogeny and SNP Variation of Polar Bears (Ursus
maritimus), Brown Bears (U. arctos), and Black Bears (U. americanus) Derived from
Genome Sequences. J Hered. 2014; 105(3):312-23. doi:10.1093/jhered/est133.
Bidon, T et al., Brown and Polar Bear Y Chromosomes Reveal Extensive Male-Biased Gene
Flow within Brother Lineages. Mol Biol Evol. 2014 Apr 4. doi:10.1093/molbev/msu109
http://blogs.biomedcentral.com/gigablog/2014/05/14/the-latest-weapon-in-publishing-data-the-polar-bear/
41. Even though the data had
been released over 2 years
earlier and cited in other
papers- the main analysis
paper was published in Cell
42. Cell Press Journals had indicated
publishing a dataset prior to publication
could be considered as prior publication
43. • New Sequencing technology
• minION Oxford-Nanopore
• New Sequence Data Type
• EBI and NCBI Databases not ready
• High community interest for testing
data
• >100 GB of data
Real time use during the
publication process
• Uploaded prior to publication
• Deployed on Amazon Cloud Front
• Ongoing
testing/comparison/information
sharing prior to publication
• When ready for data EBI used our
cloud to upload data
• EBI transferred the data to NCBI when
they were ready
45. Reproduce and Reuse Needs Much More
• Data: GigaDB
• Software: Github
• Workflows
– Galaxy
– Executable Docs
– VMs
• Images: OMERO
• Cloud storage, tools, and
compute power…
• Need this to reach the smaller
labs
github.com/gigascience/gigadb-cogini
More Journals have or are starting to introduce
these and other tools: More is needed…
47. If we want to
move
forward, we
need to go
through that
to reach this:
It will require
researchers,
institutions,
publishers,
and funders
working
together.
48. Thanks to:
Scott Edmunds, Executive Editor
Nicole Nogoy, Commissioning Editor
Peter Li, Lead Data Manager
Chris Hunter, Lead BioCurator
Rob Davidson, Data Scientist
Xiao (Jesse) Si Zhe, Database Developer
Amye Kenall, Journal Development Manager
editorial@gigasciencejournal.com
database@gigasciencejournal.com
@GigaScience
facebook.com/GigaScience
blogs.openaccesscentral.com/blogs/gigablog
Contact us:
Follow us:
www.gigasciencejournal.com
www.gigadb.org
Editor's Notes
Isn’t hyperbole fun?
Raw data has been submitted to the SRA, the assembly submitted to GenBank (no number), SV data to dbVar (it’s the first plant data they’ve received). Complements the traditional public databases by having all these “extra” data types, it’s all in one place, and it’s citable.
Raw data has been submitted to the SRA, the assembly submitted to GenBank (no number), SV data to dbVar (it’s the first plant data they’ve received). Complements the traditional public databases by having all these “extra” data types, it’s all in one place, and it’s citable.