FAIR data and model management for systems biology.

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Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.

Science

FAIR Data and Model
Management
for Systems Biology
Projects
Prof Carole Goble
FAIRDOM Consortium
The University of Manchester, UK
carole.goble@manchester.ac.uk
ISMB/ECCB 2015, Dublin, Ireland

Findable
Accessible
Interoperable
Reusable
Data
S O Ps
Models
http://www.fair-dom.org
http://www.fairdomhub.org
Assets
Aggregated
Commons for
Sys Bio Projects

Programmes, Projects, Groups
Is this data available?
What SOP was used for
this sample?
Where is the validation data for
this model?

Find
Yellow pages: projects, people
Trans-asset cataloguing & linking
Structure studies
Manage and store assets
Controlled access
Versioning
Trans-repository gateway
Deposit and download wrt
specialist public archives
Access/Archive
Interoperate
Standards compliant practices
Curation support
Tool plug-ins and API
Leverage other platforms
Analytics
Reuse/Reproduce
Consistent reporting
Simulate models with exp’mtl data
Publisher/Funder Commons with DOIs
Download, package and export
Asset Management and Sharing Platform
for Systems Biology Projects
http://www.fair-dom.org

Commons &
Cataloguing platform
Web-based cataloguing and
aggregation.
Find, Access, Describe,
Structure, Link, Exchange
project assets.
Central and distributed
repositories.
RESTAPI
LIMS & Analytics
platform
Extract,Transform and Load
tooling direct from the
instrumentation.
Data analysis pipelines,
automatic archiving.
Large data.
Funding to 2019
http://www.seek4science.org
http://www.cisd.ethz.ch/software/openBIS

Consumers
Producers
Commons
Project
Repositories
harvesting
link
Standards
organise
validate
Native
Commons
Repositories

Aggregated Commons Infrastructure
Cataloguing ++

FAIRDOM Metadata framework
link studies and link assets
Common
elements and
relationships
between things
produced and
used in
experiments.
Structured
descriptions
Consistency
Comparison
Dealing with novel
data
Just Enough
Results Model

https://doi.org/10.15490/seek.1.investigation.56
Penkler et al (2015) FEBSJ 282:1481-1511.

Linking platforms to the Commons Catalogue

RO Unzip
Snapshot into
Research Object
bundles for exchange
& publication

FAIRDOM Platform and Software Suite
Different ways of using FAIRDOM
with OpenBIS or with own project DB
Self-m’ged
Commons
Project X
Commons
Public, Hosted
Commons
FAIRDOMHub.org
Full independent
self-management on
FAIRDOMHub.org
Sept 2015

Community
Facilities
Project Support
Training and Services
Curation
Sustainability
Knowledge Networks
Forums, Workshops
Summer Schools
Working Groups
Standards
User and Developer
WGs - Samples
Partnerships
Developer Foundry
FAIRDOM developers
Free Open Source Software
VMs and Docker
Supporting projects
FAIRDOM Centres
National Infrastructures
“ISBE Light”
Working with funders
http://seek4science.github.io/seek/
de.NBI

https://sems.uni-rostock.de/reproducible-and-citable-data-and-models/

Carole Goble Stuart Owen
Finn Bacall
Jacky Snoep
Wolfgang
Mueller
Olga Krebs Quyen Nguyen
Natalie Stanford
KatyWolstencroft
Peter Kunzst Bernd Rinn
Donal FellowsAlanWilliams
Rostyslav
Kuzyakiv
Jakub
Straszewski
Chandrasekhar
Ramakrishnan
Caterina
Barillari
Norman Morrison

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Lecture 1: Being FAIR: FAIR data and model management In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation. Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects. Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester. In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face. I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects. [1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

PirithiRaju

Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

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A botany file is similar to a chemistry practical file but specifically focuses on experiments and observations related to plants. It typically includes detailed descriptions of plant-related experiments, such as germination studies, growth experiments, tissue culture techniques, plant anatomy investigations, and ecological surveys. The file may contain records of plant specimens collected, their classification, characteristics, and ecological significance. Additionally, it may include diagrams, photographs, drawings, tables, and data analysis related to plant research. Like a chemistry practical file, a botany file serves as a documentation of practical work conducted in the field of botany and is often used for assessment and educational purposes.

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FAIR data and model management for systems biology.

1. FAIR Data and Model Management for Systems Biology Projects Prof Carole Goble FAIRDOM Consortium The University of Manchester, UK carole.goble@manchester.ac.uk ISMB/ECCB 2015, Dublin, Ireland

2. Findable Accessible Interoperable Reusable Data S O Ps Models http://www.fair-dom.org http://www.fairdomhub.org Assets Aggregated Commons for Sys Bio Projects

3. Data Samples SOPs Models Publications

4. Programmes, Projects, Groups Is this data available? What SOP was used for this sample? Where is the validation data for this model?

5. 2008 Nature June 2015

6. Find Yellow pages: projects, people Trans-asset cataloguing & linking Structure studies Manage and store assets Controlled access Versioning Trans-repository gateway Deposit and download wrt specialist public archives Access/Archive Interoperate Standards compliant practices Curation support Tool plug-ins and API Leverage other platforms Analytics Reuse/Reproduce Consistent reporting Simulate models with exp’mtl data Publisher/Funder Commons with DOIs Download, package and export Asset Management and Sharing Platform for Systems Biology Projects http://www.fair-dom.org

7. Commons & Cataloguing platform Web-based cataloguing and aggregation. Find, Access, Describe, Structure, Link, Exchange project assets. Central and distributed repositories. RESTAPI LIMS & Analytics platform Extract,Transform and Load tooling direct from the instrumentation. Data analysis pipelines, automatic archiving. Large data. Funding to 2019 http://www.seek4science.org http://www.cisd.ethz.ch/software/openBIS

8. Consumers Producers Commons Project Repositories harvesting link Standards organise validate Native Commons Repositories

9. Aggregated Commons Infrastructure Cataloguing ++

10. FAIRDOM Metadata framework link studies and link assets Common elements and relationships between things produced and used in experiments. Structured descriptions Consistency Comparison Dealing with novel data Just Enough Results Model

11. https://doi.org/10.15490/seek.1.investigation.56 Penkler et al (2015) FEBSJ 282:1481-1511.

12.

13.

14. Linking platforms to the Commons Catalogue

15. FAIR Controlled Sharing & Publishing

16. RO Unzip Snapshot into Research Object bundles for exchange & publication

17. FAIRDOM Platform and Software Suite Different ways of using FAIRDOM with OpenBIS or with own project DB Self-m’ged Commons Project X Commons Public, Hosted Commons FAIRDOMHub.org Full independent self-management on FAIRDOMHub.org Sept 2015

18. Community Facilities Project Support Training and Services Curation Sustainability Knowledge Networks Forums, Workshops Summer Schools Working Groups Standards User and Developer WGs - Samples Partnerships Developer Foundry FAIRDOM developers Free Open Source Software VMs and Docker Supporting projects FAIRDOM Centres National Infrastructures “ISBE Light” Working with funders http://seek4science.github.io/seek/ de.NBI

19. https://sems.uni-rostock.de/reproducible-and-citable-data-and-models/

20. Carole Goble Stuart Owen Finn Bacall Jacky Snoep Wolfgang Mueller Olga Krebs Quyen Nguyen Natalie Stanford KatyWolstencroft Peter Kunzst Bernd Rinn Donal FellowsAlanWilliams Rostyslav Kuzyakiv Jakub Straszewski Chandrasekhar Ramakrishnan Caterina Barillari Norman Morrison

21. Find Yellow pages: projects, people Trans-asset cataloguing & linking Structure studies Manage and store assets Controlled access Versioning Trans-repository gateway Deposit into specialist public archives Access/Archive Interoperate Standards compliant practices Curation support Tool plug-ins and API Leverage other platforms Analytics Reuse/Reproduce Consistent reporting Simulate models with exp’mtl data Publisher/Funder Commons with DOIs Download, package and export Asset Management and Sharing Platform for Systems Biology Projects http://www.fair-dom.org

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