The document discusses foundation support for data science tools and skills training. It notes that while career tracks and barriers to interdisciplinary work remain unchanged, computational and data analysis skills are increasingly important for researchers. The Data-Driven Discovery Initiative aims to catalyze shifts that encourage and reward data-intensive research. This includes making data science resources more accessible and ensuring students understand data analysis by 2020. The initiative promotes tools for data-driven research and funds environments welcoming data scientists to biology.
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.
NISO Webinar on data curation services at the CDLCarly Strasser
"Building communities and Services in Support of Data-Intensive Research". Webinar on 18 Sept 2013 for the NISO Webinar Series. This was part 2 of 2 for Data Curation
Data Management for Mountain Observatories WorkshopCarly Strasser
Keynote presentation for 2014 Mountain Observatories Workshop, 16 July 2014.
Abstract:
While methods for collecting data are well taught, there is less emphasis on managing the resulting data effectively. New mandates, announcements, memos, and requirements from agencies and publishers are emerging that encourage better data management, data sharing, and data preservation. Scientists with good management skills will be able to maximize the productivity of their own research, effectively and efficiently share their data with the community, and benefit from the re-use of their data by others. I will offer an overview of data management landscape - discussing recent events, resources, and new directions for data stewardship. I will also cover best practices for data management, which will facilitate data sharing and reuse, and introduce tools researchers can use to help in their data stewardship endeavours.
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.
NISO Webinar on data curation services at the CDLCarly Strasser
"Building communities and Services in Support of Data-Intensive Research". Webinar on 18 Sept 2013 for the NISO Webinar Series. This was part 2 of 2 for Data Curation
Data Management for Mountain Observatories WorkshopCarly Strasser
Keynote presentation for 2014 Mountain Observatories Workshop, 16 July 2014.
Abstract:
While methods for collecting data are well taught, there is less emphasis on managing the resulting data effectively. New mandates, announcements, memos, and requirements from agencies and publishers are emerging that encourage better data management, data sharing, and data preservation. Scientists with good management skills will be able to maximize the productivity of their own research, effectively and efficiently share their data with the community, and benefit from the re-use of their data by others. I will offer an overview of data management landscape - discussing recent events, resources, and new directions for data stewardship. I will also cover best practices for data management, which will facilitate data sharing and reuse, and introduce tools researchers can use to help in their data stewardship endeavours.
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
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
"Undergrad ecologists aren't learning data management" - ESA 2013Carly Strasser
Presentation for Ecological Society of America 2013 Meeting in Minneapolis, MN on 6 August 2013. Results published in Ecosphere doi: 10.1890/ES12-00139.1
ESA Ignite talk on UC3 Dash platform for data sharingCarly Strasser
Ignite talk (20 slides / 15 seconds per slide) for ESA 2014 meeting in Sacramento, CA 12 August 2014. On the Dash platform for helping researchers manage and share their data via institutional repositories
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
NSF Data Management Plan - Implications for LibrariansAndrew Sallans
A. Sallans. "NSF Data Management Plan - Implications for Librarians." Presented at the Science and Technology Section (STS) Hot Topics Discussion Group Meeting of the American Library Association's 2011 Midwinter Meeting. 8 January 2011
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
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
"Undergrad ecologists aren't learning data management" - ESA 2013Carly Strasser
Presentation for Ecological Society of America 2013 Meeting in Minneapolis, MN on 6 August 2013. Results published in Ecosphere doi: 10.1890/ES12-00139.1
ESA Ignite talk on UC3 Dash platform for data sharingCarly Strasser
Ignite talk (20 slides / 15 seconds per slide) for ESA 2014 meeting in Sacramento, CA 12 August 2014. On the Dash platform for helping researchers manage and share their data via institutional repositories
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
NSF Data Management Plan - Implications for LibrariansAndrew Sallans
A. Sallans. "NSF Data Management Plan - Implications for Librarians." Presented at the Science and Technology Section (STS) Hot Topics Discussion Group Meeting of the American Library Association's 2011 Midwinter Meeting. 8 January 2011
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
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/.
This webinar will discuss the special needs of digital humanities researchers and help you learn how to talk them about their information management needs.
Topics that will be covered:
What is humanities data?
What special considerations are involved in creating DMPs for humanities data?
Where can you store humanities data?
What will humanities funding agencies be looking for? What regulations apply to humanities data (e.g., data sharing, data management, data availability)?
What librarians should know before meeting with a humanist; how humanists differ from other researchers in the way they think about their version of data.
This webinar is intended for librarians, staff, and information professionals interested in improving usability for the DMPTool in their institution. This webinar will also help institutions begin to formalize which individuals or resources will be available to help researchers using the tool. This webinar will be most useful for users that need to customize the tool for their institution.
Software development should build on the successful work of others. The DMPTool helps researchers with data management planning, but what about other phases of the data life cycle? In this webinar, we will discuss what software integration with the DMPTool might look like, and why it is important. Topics include:
1. Background: why tools integration is important; why we are talking about this in terms of the DMPTool.
2. Details and plans for DMPTool2 regarding software integration and compatibility.
3. Future possibilities for software integration for DMPTool2
4. Example of successful integration of tools: work at the Center for Open Science.
Results of a survey conducted by the Manitoba Library Associations Working Group, March 2012. Prepared for the Manitoba Libraries Conference, May 16, 2012.
International DMP workshop presentation, IDCC, Feb 2016Stephanie Simms
IDCC 2016 International Data Management Planning workshop presentation on the Smithsonian use case for the DMPTool, Amsterdam, Netherlands, 25 Feb 2016
DMPTool Webinar Series 1: Introduction to DMPTool Carly Strasser
Slides from DMPTool Webinar Series 1: Introduction to DMPTool, given 28 May 2013. Recording available at http://www.cdlib.org/services/uc3/uc3webinars.html
Researchers: how and why manage research data; CDU Darwin 070915Richard Ferrers
An ANDS(.org.au) brief presentation to Charles Darwin University researchers on research data management (RDM). What, Why and How to do RDM? Presentation 07 Sept 2015, Darwin Aust.
This presentation was provided by Sophia Lafferty-Hess of Duke University, during part one of the NISO two-part webinar "Labor and Capacity for Research Data Management," which was held on March 11, 2020.
Slides from Monday 30 July - Data in the Scholarly Communications Life Cycle Course which is part of the FORCE11 Scholarly Communications Institute.
Presenter - Natasha Simons
Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science, a Digital Research...Carole Goble
Invited talk, PHIL_OS, March 30-31 2023, Exeter
https://opensciencestudies.eu/whither-open-science. Includes hidden slides.
FAIR and Open Science needs Digital Research Infrastructure, which is a federated system of systems and needs funding models that are fit for purpose
Culture change needed for paying for Open Science’s infrastructure and funding support for data driven research needs more reality and less rhetoric
Libraries & Research Data Management for CO Alliance of Resrch LibrariesCarly Strasser
Keynote presentation for the Colorado Alliance of Research Libraries 2014 Research Data Management Conference, 11 July 2014. Focuses on why data management and sharing is important, and the role of libraries.
Open Science for Australian Institute of Marine Science WorkshopCarly Strasser
*Please excuse the typos :)
Presentation on open science and open data for the Australian Institute of Marine Science (AIMS) workshop on "Raising your research profile using research data". 18 June 2014.
Data management overview and UC3 tools for IASSIST 2014Carly Strasser
Presentation to introduce current landscape of data management and UC3 tools and services that support data sharing. For IASSIST in Toronto, 5 June 2014.
Data Publication for UC Davis Publish or PerishCarly Strasser
Intro presentation for panel on going beyond publishing journal articles. UC Davis "Publish or Perish?" Event, 13 Feb 2014. Sorry about missing gradient on some of slides!
October 18, 2013 @ Kennedy Library, Data Studio, Cal Poly. We hear about all things “open” these days: open access, open source, open data, open science, et cetera. But what does it really mean for how we do science? How are things changing, and what are the implications for individual researchers?
Cal Poly - Data Management: Who knew it was a hot topic?Carly Strasser
October 17, 2013 @ Robert E. Kennedy Library, Data Studio, California Polytechnic State University.
New mandates, announcements, memos, and requirements are emerging that encourage better data management, data sharing, and data preservation. In this presentation, data curation specialist Carly Strasser, PhD, offers a lay of the data management land by discussing recent events, resources, and new directions for data stewardship.
Cal Poly - Data Management and the DMPToolCarly Strasser
October 17, 2013 @ Robert E. Kennedy Library, Data Studio, California Polytechnic State University.
Many funders now require researchers to submit a Data Management Plan alongside their project proposals. The DMPTool is a free, online wizard that helps you create a data management plan specific to your project, and provides you with links and resources for ensuring your plan is successful.
Cal Poly - Data Management for ResearchersCarly Strasser
October 17, 2013 @ 1 Robert E. Kennedy Library, Data Studio, California Polytechnic State University.
Researchers rarely learn about good data management practices. Instead we develop our own systems that are often unintelligible to others. In this talk, Strasser, PhD, will focus on the common mistakes that scientists make and how to avoid them. She will provide best practices for data management, which will facilitate data sharing and reuse, and introduce tools you can use.
Overview of data management policies and data management plans, including the DMPTool. For Ecological Society of America 2013 Meeting in Minneapolis, MN 5 August 2013.
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.
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.
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.
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 .
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.
4. What hasn’t changed:
Career tracks
Barriers to interdisciplinary work
Credit & incentives
Training
Leaky pipeline for
computationally-savvy
researchers
From Flickr by Willie Angus
5. DDD Goal:
Catalyze shifts towards new norms
Academic institutions nurture/reward data-
intensive research(ers)
Tools & practices are available,
disseminated, and used
9. “In 2020, every college student should
be prepared to understand and develop
points of view based on the analysis of
data as well as evaluate arguments
made by others”
Formal
Informal
12. tl;dr: We are organizing an event where trainees launch
collaborative projects.
…Our goal is to host an event that will generate projects that
integrate the skills and expertise of at least two (ideally more)
DDD-I labs…
Barnraising
Gene expression Computer
science
Astrophysics
…DDD
Training Club
17. What we ARE doing:
• Funding data science environments that welcome biologists
• Supporting investigators in biology and life sciences who
demonstrate the skills needed
• Funding projects that enable and promote training the biology
workforce
• Getting involved in discussions about training data scientists
What we ARE NOT doing:
• Directly funding Biology-focused projects/tools
• Directly funding formal data science education