Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
Written and presented by Carole Goble (University of Manchester) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
Presentation given at the NBT / ECCB 2020, presenting COMBINE standards. Also providing links to related projects, introducing open model repositories and giving some hints for creating reusable models.
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
An experimental comparison of globally-optimal data de-identification algorithmsarx-deidentifier
Collaboration and data sharing have become core elements of biomedical research. At the same time, there is a growing understanding of privacy threats related to data sharing, especially when sensitive data from distributed sources become available for linkage. Statistical disclosure control comprises well-known data anonymization techniques that allow the protection of data by introducing fuzziness. To protect datasets from different types of threats, different privacy criteria are commonly implemented. Data anonymization is an important measure, but it is computationally complex, and it can significantly reduce the expressiveness of data. To attenuate these problems, a number of algorithms has been proposed, which aim at increasing data quality or improving efficiency. Previous evaluations of such algorithms lack a systematic approach, as they focus on specific algorithms, specific privacy criteria, and specific runtime environments. Therefore, it is difficult for decision makers to decide which algorithm is best suited for their requirements. As a first step towards a comprehensive and systematic evaluation of anonymity algorithms, we report on our ongoing efforts for providing an open source benchmark. In this contribution, we focus on optimal algorithms utilizing global recoding with full-domain generalization. We present a systematic evaluation of domain-specific algorithms and generic search methods for a broad set of privacy criteria, including k-anonymity, l-diversity, t-closeness and d-presence, and their use in multiple real-world datasets. Our results show that there is no single solution fitting all needs, and that generic search methods can outperform highly specialized algorithms.
A global integrative ecosystem for digital pathology: how can we get there?Yves Sucaet
Digital pathology has many faces. Its stakeholders can roughly be classified into four categories: education, research, clinical, and clinical research. We come together at events like Pathology Informatics or Pathology Visions, and discuss the evolution of the field.
While progression is being made, it sometimes appears that around every corner are more challenges and forks in the road. New applications and scenarios emerge at a rapid pace, and it is clear that a single one-size-fits-all type of software is unlikely to satisfy most participants in this space, if any.
At the institutional level, ecosystems of digital pathology have already been established. At a national level, attempts are being made. At a global level, this is still a wide open question, but one very much worth exploring.
Digital pathology comes with some unique properties, like the data it generates and the pace at which this happens. This guest lecture then will examine the solutions that already exist, and what an inclusive global scalable digital pathology ecosystem may look like in the future.
Engineering data privacy - The ARX data anonymization toolarx-deidentifier
Website with further information: http://arx.deidentifier.org
Description of this talk:
While a plethora of methods have been proposed for dealing with many aspects of de-identifying clinical data, only few (prototypical) implementations are available. Actually, the complexity of implementing privacy technologies is an often overlooked challenge.
In this talk we will present the open source data de-identification tool ARX, which has been carefully engineered to support multiple privacy technologies for relational datasets. Our tool bridges the gap between different scientific disciplines by integrating methods developed and used by the statistics community with data anonymization techniques developed by computer scientists.
ARX has been designed from the ground up to ensure scalability and it is able to process very large datasets on commodity hardware. The software implements a large set of
privacy models: (1) syntactic privacy models, such as k-anonymity, l-diversity, t-closeness and δ-presence, (2) statistical models for re-identification risks, and (3) differential privacy. In the talk, we will focus on measures to reduce the uniqueness of records. ARX also supports more than ten different methods for evaluating data utility, including loss, precision, non-uniform entropy and KL divergence.
In ARX, de-identification of data can be performed automatically, semi-automatically and manually using a complex method that integrates global recoding, local recoding, categorization, generalization, suppression, microaggregation and top/bottom-coding. All methods are accessible via a comprehensive cross-platform graphical user interface.
Citing data in research articles: principles, implementation, challenges - an...FAIRDOM
Prepared and presented by Jo McEntyre (EMBL_EBI) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
Over the past 5 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 and so forth. Don’t stop reading. Yes, data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. And the multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
ARX - a comprehensive tool for anonymizing / de-identifying biomedical dataarx-deidentifier
Website with further information: http://arx.deidentifier.org
Description of this talk:
Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present the data anonymization tool ARX, which has been developed in close cooperation between the Chair for Biomedical Informatics, the Chair for IT Security and the Chair for Database Systems at Technische Universität München (TUM), Germany. ARX enables the de-identification of structured data (i.e., tabular data) and implements a wide variety of privacy methods in a highly efficient manner. It is extensible, well documented and actively supported. ARX provides an intuitive cross-platform graphical interface and offers a public API for integration with other software systems.
ACS 248th Paper 136 JSmol/JSpecView Eureka IntegrationStuart Chalk
Integration of the combined JSmol/JSpecView molecular viewer/spectral viewer software in the Eureka Research Workbench. Can display molecular structures, spectra and the linked version where clicking on a peak shows molecular movement (IR).
Advancing Healthy Food Access Through Regional PartnershipsNFCACoops
The NFCA, Cooperative Fund of New England, and Hunger Free Vermont have worked with NFCA members to implement programs addressing food access and community ownership. Since 2014 seven NE food co-ops have implemented new “Food For All” programs, making healthy food and co-op ownership more accessible to people with limited incomes. This presentation covers how regional co-op collaboration and strategic partnerships helped neighboring food co-ops across New England address food access, enhance community identity, increase the collective impact of co-ops on food security, and how working with USDA helps ensure this model is sustainable and replicable across the country.
Wordpress: Make Your Site Impressively BeautifulMafel Gorne
Today, I am so excited to share to you another step by step tutorial and that is “How to create a free website with Wordpress”
Buddy, what is Wordpress? It is a software where you can create any kind of website.
Let me give you a quick and simple step by step tutorial on how to create a free website with Wordpress.
Let us begin, My Browsing Buddy!
A colorful visual tour of the Dia De Los Muertos or Day of The Dead ritual that took place at Hollywood Forever Cemetery in Hollywood, Ca. on Saturday, October 24, 2015.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
Presentation given at the NBT / ECCB 2020, presenting COMBINE standards. Also providing links to related projects, introducing open model repositories and giving some hints for creating reusable models.
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Written and presented by Wolfgang Müller (HITS) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
An experimental comparison of globally-optimal data de-identification algorithmsarx-deidentifier
Collaboration and data sharing have become core elements of biomedical research. At the same time, there is a growing understanding of privacy threats related to data sharing, especially when sensitive data from distributed sources become available for linkage. Statistical disclosure control comprises well-known data anonymization techniques that allow the protection of data by introducing fuzziness. To protect datasets from different types of threats, different privacy criteria are commonly implemented. Data anonymization is an important measure, but it is computationally complex, and it can significantly reduce the expressiveness of data. To attenuate these problems, a number of algorithms has been proposed, which aim at increasing data quality or improving efficiency. Previous evaluations of such algorithms lack a systematic approach, as they focus on specific algorithms, specific privacy criteria, and specific runtime environments. Therefore, it is difficult for decision makers to decide which algorithm is best suited for their requirements. As a first step towards a comprehensive and systematic evaluation of anonymity algorithms, we report on our ongoing efforts for providing an open source benchmark. In this contribution, we focus on optimal algorithms utilizing global recoding with full-domain generalization. We present a systematic evaluation of domain-specific algorithms and generic search methods for a broad set of privacy criteria, including k-anonymity, l-diversity, t-closeness and d-presence, and their use in multiple real-world datasets. Our results show that there is no single solution fitting all needs, and that generic search methods can outperform highly specialized algorithms.
A global integrative ecosystem for digital pathology: how can we get there?Yves Sucaet
Digital pathology has many faces. Its stakeholders can roughly be classified into four categories: education, research, clinical, and clinical research. We come together at events like Pathology Informatics or Pathology Visions, and discuss the evolution of the field.
While progression is being made, it sometimes appears that around every corner are more challenges and forks in the road. New applications and scenarios emerge at a rapid pace, and it is clear that a single one-size-fits-all type of software is unlikely to satisfy most participants in this space, if any.
At the institutional level, ecosystems of digital pathology have already been established. At a national level, attempts are being made. At a global level, this is still a wide open question, but one very much worth exploring.
Digital pathology comes with some unique properties, like the data it generates and the pace at which this happens. This guest lecture then will examine the solutions that already exist, and what an inclusive global scalable digital pathology ecosystem may look like in the future.
Engineering data privacy - The ARX data anonymization toolarx-deidentifier
Website with further information: http://arx.deidentifier.org
Description of this talk:
While a plethora of methods have been proposed for dealing with many aspects of de-identifying clinical data, only few (prototypical) implementations are available. Actually, the complexity of implementing privacy technologies is an often overlooked challenge.
In this talk we will present the open source data de-identification tool ARX, which has been carefully engineered to support multiple privacy technologies for relational datasets. Our tool bridges the gap between different scientific disciplines by integrating methods developed and used by the statistics community with data anonymization techniques developed by computer scientists.
ARX has been designed from the ground up to ensure scalability and it is able to process very large datasets on commodity hardware. The software implements a large set of
privacy models: (1) syntactic privacy models, such as k-anonymity, l-diversity, t-closeness and δ-presence, (2) statistical models for re-identification risks, and (3) differential privacy. In the talk, we will focus on measures to reduce the uniqueness of records. ARX also supports more than ten different methods for evaluating data utility, including loss, precision, non-uniform entropy and KL divergence.
In ARX, de-identification of data can be performed automatically, semi-automatically and manually using a complex method that integrates global recoding, local recoding, categorization, generalization, suppression, microaggregation and top/bottom-coding. All methods are accessible via a comprehensive cross-platform graphical user interface.
Citing data in research articles: principles, implementation, challenges - an...FAIRDOM
Prepared and presented by Jo McEntyre (EMBL_EBI) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
Over the past 5 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 and so forth. Don’t stop reading. Yes, data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. And the multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
ARX - a comprehensive tool for anonymizing / de-identifying biomedical dataarx-deidentifier
Website with further information: http://arx.deidentifier.org
Description of this talk:
Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present the data anonymization tool ARX, which has been developed in close cooperation between the Chair for Biomedical Informatics, the Chair for IT Security and the Chair for Database Systems at Technische Universität München (TUM), Germany. ARX enables the de-identification of structured data (i.e., tabular data) and implements a wide variety of privacy methods in a highly efficient manner. It is extensible, well documented and actively supported. ARX provides an intuitive cross-platform graphical interface and offers a public API for integration with other software systems.
ACS 248th Paper 136 JSmol/JSpecView Eureka IntegrationStuart Chalk
Integration of the combined JSmol/JSpecView molecular viewer/spectral viewer software in the Eureka Research Workbench. Can display molecular structures, spectra and the linked version where clicking on a peak shows molecular movement (IR).
Advancing Healthy Food Access Through Regional PartnershipsNFCACoops
The NFCA, Cooperative Fund of New England, and Hunger Free Vermont have worked with NFCA members to implement programs addressing food access and community ownership. Since 2014 seven NE food co-ops have implemented new “Food For All” programs, making healthy food and co-op ownership more accessible to people with limited incomes. This presentation covers how regional co-op collaboration and strategic partnerships helped neighboring food co-ops across New England address food access, enhance community identity, increase the collective impact of co-ops on food security, and how working with USDA helps ensure this model is sustainable and replicable across the country.
Wordpress: Make Your Site Impressively BeautifulMafel Gorne
Today, I am so excited to share to you another step by step tutorial and that is “How to create a free website with Wordpress”
Buddy, what is Wordpress? It is a software where you can create any kind of website.
Let me give you a quick and simple step by step tutorial on how to create a free website with Wordpress.
Let us begin, My Browsing Buddy!
A colorful visual tour of the Dia De Los Muertos or Day of The Dead ritual that took place at Hollywood Forever Cemetery in Hollywood, Ca. on Saturday, October 24, 2015.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
Model Management in Systems Biology: Challenges – Approaches – SolutionsMartin Scharm
I gave this talk as a webinar in the FAIRDOM webinar series 2016. The recordings of the webinar are available from http://fair-dom.org/knowledgehub/webinars-2/martin-scharm/
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
Standards and software: practical aids for reproducibility of computational r...Mike Hucka
My presentation during the session titled "Reproducibility of computational research: methods to avoid madness" on Wednesday, 17 September 2014, during ICSB 2014, held in Melbourne, Australia.
Confessions of an Interdisciplinary Researcher: The Case of High Performance ...tiberiusp
Scaling up economics models to run on large input sizes, complex market and agent model settings, and on big computational resource pools is a demanding feat.
This presentation tells you what it takes to work as a computational economist.
FIWARE Training: Introduction to Smart Data ModelsFIWARE
An online training course run by the FIWARE Foundation in conjunction with the i4Trust project and IShare Foundation. The core part of this virtual training camp (27 Jun - 01 Jul 2022) covered all the necessary skills to develop smart solutions powered by FIWARE. It introduces the basis of Digital Twin programming using NGSI-LD (the simple yet powerful open standard API enabling to publish and access digital twin data) combined with common smart data models
In addition, it covers the supplementary FIWARE technologies used to implement the rest of functions typically required when architecting a complete smart solution: Identity and Access Management (IAM) functions to secure access to digital twin data, and functions enabling the interface with IoT and 3rd systems, or the connection with different tools for processing and monitoring current and historic big data.
Extending this core part, the training camp also cover how you can easily integrate FIWARE systems with blockchain networks to create audit-proof logs of processes and ensure transparency.
FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble
FAIR Data, Operations and Model management for Systems Biology and Systems Medicine Projects given at 1st Conference of the European Association of Systems Medicine, 26-28 October 2016, Berlin. the FAIRDOM project is described.
Integrative Programming Technology Chapter 5 - Dr. J. VijiPriyaVijiPriya Jeyamani
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Integrative Coding: Design Patterns; Interfaces; Inheritance
Miscellaneous Issues: Adopt and Adapt vs. make; Versioning and version control
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The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
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Exposé invité Journées Nationales du GDR GPL 2024
Deep Software Variability and Frictionless Reproducibility
Data and Model Management for Systems Biology
1. Data and Model Management
for Systems Biology
Dagmar Waltemath
http://sems.uni-rostock.de
Slides available from www.slideshare.net/dagwa/
2. Developing a computational model
… may take a long time
… may involve different research groups
… may involve different tools
… may involve different modeling approaches
… may lead to different versions of a model
… will result in a bunch of files
… will result in a publication
3. Journals ask for model code
The Physiome Model Repository
https://models.cellml.org/
Yu et al (2011)
BioModels
http://www.ebi.ac.uk/biomodels-main/
Li et al (2010)
4. Journals ask for model code
Fig.: Curation and publication processes in BioModels
(Figure courtesy Vijayalakshmi Chelliah)
5. Journals ask for model code
Fig.: Curation and publication processes in BioModels
(Figure courtesy Vijayalakshmi Chelliah)
Manage your data throughout
(collaborative) projects
Prepare your model-related
data for publication
Archive and document
model-related data
Get information about a
model's evolution
Extract all data related
to a modeling result
Find relevant models
Publish Reproduce & Reuse
8. What data belongs to a model?
● the model(s) themselves
● the semantic annotations describing the model and its
components
● the simulation setups and model parametrisation
● experimental data used to feed the model
● result data (tables, figures)
● reference publication
+ links between these files
See also: Henkel et al (2015) Combining computational models, … DATABASE
9. Manage your data
Personal Data
Local Stores
External
Databases
Articles
Standards
SOPs
→ Hands on
Slide courtesy Wolgang Müller. See also: Wolstencraft et al (2015) BMC Systems Biology
10. Prepare for publication
See also: Waltemath et al (2013) Reproducibility of model-based results in systems biology (Springer)
● Export your model in a standard format.
● Annotate model components with links to bio-ontologies.
– Use Gene Ontology, ChEBI etc for entities
– Use SBO for mathematical terms
– Use TEDDY for behavior
– Provide provenance information (model origin, author, creator,
modification dates, software)
– Link to original publication
11. See also: Waltemath et al (2013) Reproducibility of model-based results in systems biology (Springer)
● Provide the simulation recipe for each analysis you describe in the paper.
– Simulation setup
– Model parametrisation
– Algorithm used in analysis (Software)
– Output
● Store the simulation setup in standard format.
● Try to reproduce the results from the information intended to publish.
● Ask a colleague to try and reproduce the results.
– Every piece of information necessary for that person to reproduce the findings
should be included in the supplementary information.
● Pack all necessary data files into an archive format and submit to a public
database to receive a URI.
Prepare for publication
12. Archive your results
See also: Bergmann et al (2014) COMBINE archive and OMEX format. BMC Bioinformatics
● COMBINE Archive
– Zip-like format to bundle
all model related files in
one place
– Eases the upload of a
modeling study to
BioModels
– Tells curators what to do
with the files (manifest)
– Eases exchange of files
with collaborators
→ Hands on
13. Find models of interest
The safe way The experimental way
– Use MaSyMoS to search for
models (through Cypher
queries or M2CAT)
– Try ranked retrieval in PMR2
Figure courtesy Vijayalakshmi Chelliah
→ BioModels hands on
14. Extract reproducible
simulation experiments
The safe way
– Use SEEK for
model management
– Export Research Objects
The experimental way
– Create archives with the
COMBINE Archive Toolkit
– Extract archives from
MaSyMoS using M2Cat
internet
internet
internet
SEARCHubiquitin
internet
RESULTS
EXPORT
EXPORT
EXPORT
EXPORT
Query database
for annotations, persons,
simulation descriptions
Retrieve information
about models, simulations,
figures, documentation
Export simulation study
as COMBINE archive
Download archive
and open the study
with your favourite
simulation tool
Open archive in CAT
to modify its contents and
to share it with others
internet
API Commincations
enrich your studies
with simulation results
Simulate a Study
with just a single click
Fig.: Waltemath and Scharm (2015) BTWFigure taken from http://researchobjects.org
15. Get information about
the model's evolution
The safe way
– Use SEEK to look at
model versions
– Check releases of
BioModels or exposures
in PMR2
The experimental way
– Compare model versions
using BiVeS
Fig.: Visualising differences between model versions
in BiVeS. See also Scharm et al (2015) BIOINFORMATICS.
Fig.: Example of a model's history in PMR2.
16. SEEK: Data and model
management out of the box
●
Questions we'll be answering in the afternoon hands on:
– What kind of data can SEEK manage?
– How can we manage and simulate models in SEEK?
– What is the ISA-structure?
– How can we share models inside SEEK and how can we export them?
●
Please keep the files you generate throughout the
SABIO-RK, COPASI, and SYCAMORE tutorials
●
Please register at our sandbox version of SEEK:
https://denbi-school.fairdomhub.org/projects/3
17. Thank you for your attention.
● See you later → Please register with SEEK at
https://denbi-school.fairdomhub.org/projects/3
● Else → Check out http://sems.uni-rostock.de for further
information on how to make your research reproducible.