Making your data good enough for sharing.

Sep 25, 2015Download as PPTX, PDF0 likes4,812 views

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.

On Golf and Data
Wolfgang Müller
Making your data good
enough for sharing

Challenge of data sharing
• Most data never gets shared
– Wrong experimental method
– Hidden parameter discovered
– Faulty experiment
• How to prepare data in this situation?
– Don‘t want to waste time
– Want to be prepared if we share
• Propose useful way forward

Making your data good enough for sharing.

80-20 rule
Voltaire: „The best is the enemy of the good“
80-20 rule: Often you can get 80% of the
benefits using 20% of the effort.
Tee-off Approach
Put-
ting
Biggest
approach
in one shot

What to share?
• Raw data (sometimes)
• Condensed, interpreted data
• Metadata: Data about the data
– Conditions of the measurements
– Information about the samples
• What was sampled?
• How was it prepared?
• How was it treated after sampling?

Levels of detail
• Action guidelines (e.g. SOP)
• Structure guidelines (e.g. F1000 data
preparation guidelines)
• Semantics guidelines (metadata + content,
e.g. some MIBBIs)
• File format standards (e.g. ISA-TAB, SBML)
• Ontologies + vocabularies (e.g. ChEBI)

Standardisation scales
• Self
• Group
• Collaborative project
• Field scale
Increasedusabilityforothers

Self-standardisation
• Store same things in same structure
– Test question: „Does Excel cell (e.g.) A2 have the same
meaning in all files about the same experiment type“?
• Name same things the same way
– Test question: „Does ‚gl‘ mean exactly the same in all
occurences“?
• Identify uniquely things that you reference.
Benefit:
Automatic adaptation of your data much easier

Identify uniquely
(e.g. McCurry et al. preprint)
1. If you create identifiers, do not DIY (Do Identifiers by
Yourself)
2. Help identifiers travel well: don’t let them leave home
without a Prefix and a Namespace
3. Make Local Resource Identifiers rugged to realworld use
4. Make the full URI simple and durable
5. Carefully consider whether to embed meaning
6. Make the full URI and CURIE clear and easy to find
7. Implement a version management policy
8. Manage complex lifecycles without deletion
9. Document the identifiers you issue and use
10. Reference responsibly and rely on full URIs

Standardisation within group or
project
Same as before, but in addition:
• Needs agreeing on how to do things
the same way
• Needs looking into standards for your domain
– Inspiration how to proceed
– Clear insight into migration paths

e.g. F1000 data preparation guidelines
• Give each column a descriptive heading
• Use a single header row
• Ensure you have used the first cell, i.e. A1
• Include Title & Legend for each spreadsheet
• Save each data file with a telling name
• Submit each table as a separate file
• Submit each work sheet as a separate file

Systems Biology Markup Language
• XML-Based format
– Levels and Versions
– Packages
• Model of relations within SBML files as UML
• Library implementations
• MIRIAM guidelines for proper annotation of
SBML files
• MIRIAM resources, MIRIAM resolver
for providing identifiers and links
• ...

The two-day Systems Biology Data Management Foundry Workshop brought together 35 participants from 5 countries to improve collaboration among data management practitioners and explore opportunities in systems biology, synthetic biology, and systems medicine. Participants gained a better understanding of different systems through show-and-tell sessions, generated ideas for cross-integration, and discussed establishing a foundry to support developers. Outcomes included forming collaborations and planning for future meetings to continue developing solutions for open, interoperable, and reusable data management.

Reflections on a (slightly unusual) multi-disciplinary academic careerCarole Goble

Reproducible Research: how could Research Objects helpCarole Goble

The FAIRDOM Commons for Systems BiologyFAIRDOM

Reproducibility (and the R*) of Science: motivations, challenges and trendsCarole Goble

Reproducible and citable data and models: an introduction.FAIRDOM

FAIRy stories: tales from building the FAIR Research CommonsCarole Goble

Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org Title: FAIRy stories: tales from building the FAIR Research Commons Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.

Introduction to FAIRDOMCarole Goble

This document introduces FAIRDOM, a consortium that provides a platform and services to help researchers organize, manage, share, and preserve research outputs according to FAIR principles. FAIRDOM has been in operation for 10 years and has over 50 installations supporting over 118 projects. It provides tools and services to help researchers collaborate better and integrate their data, models, publications and other research objects. FAIRDOM also works with other organizations and infrastructure providers to support broader research initiatives.

Research Objects, SEEK and FAIRDOMCarole Goble

FAIR data and model management for systems biology.FAIRDOM

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

Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble

Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session. In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers. The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme. 2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes. In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust. [1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18 [2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032

What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble

Capturing the context: one small(ish step for modellers, one giant leap for m...FAIRDOM

Let’s go on a FAIR safari!Carole Goble

COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019 FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers. Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine? Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines. So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine. References [1] http://www.fair-dom.org [2] http://www.fairdomhub.org [3] http://www.biocomputeobject.org

FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble

This document discusses the FAIRDOM consortium's efforts to promote FAIR (Findable, Accessible, Interoperable, Reusable) principles for managing data, operations, and models from systems biology and systems medicine projects. It outlines challenges in asset management for multi-partner, multi-disciplinary projects using multiple formats and repositories. FAIRDOM provides pillars of support including community actions, platforms/tools, and a public project commons to help address these challenges and better enable sharing, reuse, and reproducibility of research assets according to FAIR principles.

Better Software, Better ResearchCarole Goble

Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM

Crediting informatics and data folks in life science teamsCarole Goble

Improving the Management of Computational Models -- Invited talk at the EBIMartin Scharm

Research Shared: researchobject.orgNorman Morrison

This document discusses Research Objects (RO), which provide a framework for bundling, exchanging, and linking resources related to experiments in order to improve reproducibility. The RO framework uses unique identifiers, aggregation, and metadata to group related resources. Real-world examples of ROs include reviewed scientific papers, workflow runs, and Docker images. ROs can help make research fully FAIR (Findable, Accessible, Interoperable, Reusable). Tools and platforms like FAIRDOM, SEEK, and Figshare support the use of ROs.

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble

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. 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. 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. The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions. http://www.fair-dom.org http://www.fairdomhub.org http://www.seek4science.org Presented at COMBINE 2016, Newcastle, 19 September. http://co.mbine.org/events/COMBINE_2016

Reproducibility, Research Objects and Reality, Leiden 2016Carole Goble

Presented at the Leiden Bioscience Lecture, 24 November 2016, Reproducibility, Research Objects and Reality 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, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship 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. It all sounds very laudable and straightforward. BUT….. Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices. In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange In this talk I will explore these issues in data-driven computational life sciences through the examples and stories from initiatives I am involved, and Leiden is involved in too including: · FAIRDOM which has built a Commons for Systems and Synthetic Biology projects, with an emphasis on standards smuggled in by stealth and efforts to affecting sharing practices using behavioural interventions · ELIXIR, the EU Research Data Infrastructure, and its efforts to exchange workflows · Bioschemas.org, an ELIXIR-NIH-Google effort to support the finding of assets.

Mtsr2015 goble-keynoteCarole Goble

Metadata and Semantics Research Conference, Manchester, UK 2015 Research Objects: why, what and how, In practice the exchange, reuse and reproduction of scientific experiments is hard, dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: codes fork, data is updated, algorithms are revised, workflows break, service updates are released. Neither should they be viewed just as second-class artifacts tethered to publications, but the focus of research outcomes in their own right: articles clustered around datasets, methods with citation profiles. Many funders and publishers have come to acknowledge this, moving to data sharing policies and provisioning e-infrastructure platforms. Many researchers recognise the importance of working with Research Objects. The term has become widespread. However. What is a Research Object? How do you mint one, exchange one, build a platform to support one, curate one? How do we introduce them in a lightweight way that platform developers can migrate to? What is the practical impact of a Research Object Commons on training, stewardship, scholarship, sharing? How do we address the scholarly and technological debt of making and maintaining Research Objects? Are there any examples I’ll present our practical experiences of the why, what and how of Research Objects.

How are we Faring with FAIR? (and what FAIR is not)Carole Goble

Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020 https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/ Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good). In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.

THOR Workshop - IntroductionMaaike Duine

The document discusses the THOR project, which aims to place Persistent Identifiers (PIDs) at the fingertips of researchers and integrate them into existing research services and outputs. The goals are to uniquely attribute work to researchers and make PID use the default across the research lifecycle. The project focuses on biological sciences, earth sciences, physical sciences, social sciences, and humanities. It provides examples of how PIDs can improve credit for researchers, discoverability and reuse of data and publications, demonstrate value for data centers, improve evidence for publishers, and measure impact for funders.

OSFair2017 Workshop | How FAIR friendly is the FAIRDOM Hub? Exposing metadata...Open Science Fair

Carole Goble presents the FAIRDOM | OSFair2017 Workshop Workshop title: How FAIR friendly is your data catalogue? Workshop overview: This workshop will build upon the work planned by the EOSCpilot data interoperability task and the BlueBridge workshop held on April 3 at the RDA meeting. We will investigate common mechanisms for interoperation of data catalogues that preserve established community standards, norms and resources, while simplifying the process of being/becoming FAIR. Can we have a simple interoperability architecture based on a common set of metadata types? What are the minimum metadata requirements to expose FAIR data to EOSC services and EOSC users? DAY 3 - PARALLEL SESSION 6 & 7

THOR Workshop - Data Publishing ElsevierMaaike Duine

Elsevier supports researchers in sharing their data through several programs and services: 1. A data-linking program connects articles to over 60 domain-specific data repositories through in-article data accession numbers and banners. 2. Mendeley Data is Elsevier's research data repository, allowing researchers to store, share and publish research data with a DOI and link to related articles. 3. In-article data visualization tools display plot data from supplementary materials in journals, allowing readers to access, explore and download underlying data.

Improving the management of computational models.FAIRDOM

Citing data in research articles: principles, implementation, challenges - an...FAIRDOM

More Related Content

What's hot (20)

Research Objects, SEEK and FAIRDOMCarole Goble

FAIR data and model management for systems biology.FAIRDOM

FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble

Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble

What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble

Capturing the context: one small(ish step for modellers, one giant leap for m...FAIRDOM

Let’s go on a FAIR safari!Carole Goble

FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble

Better Software, Better ResearchCarole Goble

Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM

Crediting informatics and data folks in life science teamsCarole Goble

Improving the Management of Computational Models -- Invited talk at the EBIMartin Scharm

Research Shared: researchobject.orgNorman Morrison

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble

Reproducibility, Research Objects and Reality, Leiden 2016Carole Goble

Mtsr2015 goble-keynoteCarole Goble

How are we Faring with FAIR? (and what FAIR is not)Carole Goble

THOR Workshop - IntroductionMaaike Duine

OSFair2017 Workshop | How FAIR friendly is the FAIRDOM Hub? Exposing metadata...Open Science Fair

THOR Workshop - Data Publishing ElsevierMaaike Duine

Research Objects, SEEK and FAIRDOMCarole Goble

FAIR data and model management for systems biology.FAIRDOM

FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble

Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble

What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble

Capturing the context: one small(ish step for modellers, one giant leap for m...FAIRDOM

Let’s go on a FAIR safari!Carole Goble

FAIR Data, Operations and Model management for Systems Biology and Systems Me...Carole Goble

Better Software, Better ResearchCarole Goble

Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM

Crediting informatics and data folks in life science teamsCarole Goble

Improving the Management of Computational Models -- Invited talk at the EBIMartin Scharm

Research Shared: researchobject.orgNorman Morrison

FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...Carole Goble

Reproducibility, Research Objects and Reality, Leiden 2016Carole Goble

Mtsr2015 goble-keynoteCarole Goble

How are we Faring with FAIR? (and what FAIR is not)Carole Goble

THOR Workshop - IntroductionMaaike Duine

OSFair2017 Workshop | How FAIR friendly is the FAIRDOM Hub? Exposing metadata...Open Science Fair

THOR Workshop - Data Publishing ElsevierMaaike Duine

Viewers also liked (7)

Improving the management of computational models.FAIRDOM

Citing data in research articles: principles, implementation, challenges - an...FAIRDOM

Licensing, Citation and Sustainability.FAIRDOM

The document discusses licensing, citation, and sustainability of intellectual property. It covers different types of licenses for software and data including open source, proprietary, and Creative Commons licenses. It provides resources for choosing an appropriate license, ensuring works are properly cited and credited to help sustain them, and guidelines for repositories, audits, and certifications.

FAIR data and model management for systems biology (and SOPs too!)FAIRDOM

Publishing data and code openlyFAIRDOM

Advances in Scientific Workflow EnvironmentsCarole Goble

ERA CoBioTech Data Management WebinarFAIRDOM

The webinar discussed FAIRDOM services that can help applicants to the ERACoBioTech call with their data management plans and requirements. FAIRDOM offers webinars on developing data management plans, and their platform and tools can help with organizing, storing, sharing, and publishing research data and models in a FAIR manner by utilizing metadata standards. Different levels of support are available, from general community resources through their hub, to premium customized support for individual projects. Consortia can include FAIRDOM as a subcontractor within the guidelines of the ERACoBioTech call.

Improving the management of computational models.FAIRDOM

Citing data in research articles: principles, implementation, challenges - an...FAIRDOM

Licensing, Citation and Sustainability.FAIRDOM

FAIR data and model management for systems biology (and SOPs too!)FAIRDOM

Publishing data and code openlyFAIRDOM

Advances in Scientific Workflow EnvironmentsCarole Goble

ERA CoBioTech Data Management WebinarFAIRDOM

Similar to Making your data good enough for sharing. (20)

Best practices data collectionSherry Lake

This document outlines best practices for creating research data. [1] It recommends using consistent data organization with standardized formats and descriptive file names. [2] Researchers should perform quality assurance checks and use scripted programs to analyze data while keeping notes. [3] All aspects of data collection and analysis should be thoroughly documented. Following these practices will improve data usability, sharing, and reproducibility.

Best practices data managementSherry Lake

1. The document discusses best practices for managing research data over the data life cycle, from collection through sharing and archiving. It provides tips for organizing, documenting, and storing data in sustainable file formats and naming conventions. Following best practices helps ensure usability, reproducibility, and long-term access to research data. 2. Specific best practices covered include using consistent organization, standardized naming and formats, descriptive filenames, quality assurance, scripting for processing, documenting file contents, and choosing open file formats. The document also addresses data security, backup, and storage considerations. 3. Managing data properly is important for reuse and sharing data with others now or in the future. Scripting helps capture data workflows for reproducibility.

Planning for Research Data Management: 26th January 2016IzzyChad

This document provides an overview of a session on planning for research data management. It discusses what research data management is, why it is important, and walks through the steps for creating a data management plan. The presenter explains the benefits of effective data management, such as helping researchers work more efficiently and enabling data sharing. Key aspects of a data management plan are also outlined, including describing the data, addressing ethics and intellectual property, determining how data will be stored and preserved, and making plans for data sharing and access.

Take control of your PhD journey: Manage your research data according to best...Lars Figenschou

Data Archiving and SharingC. Tobin Magle

Planning for Research Data Managementdancrane_open

This document provides an overview of research data management and outlines the steps for creating a data management plan. It discusses why research data management is important, including enabling data reuse and sharing and meeting funder requirements. The document then walks through creating a data management plan, covering topics like the types and formats of data that will be generated, ethical and intellectual property issues, how data will be stored and backed up, and long-term preservation and deposition of data. It emphasizes that planning early helps ensure accurate, complete and secure data, and avoids problems down the line.

Data presentation and transferIyad Abou Rabii

1) The document discusses best practices for managing research data, including organizing files, documenting data with metadata, storing data securely both internally and externally, and presenting data through tables, charts, and text for publication and sharing. 2) Key recommendations for data management include using logical file naming conventions, non-proprietary file formats, and documenting data with standard metadata fields. External repositories can increase data accessibility and preservation. 3) Effective data presentation involves using tables and charts to clearly visualize quantitative and qualitative findings. Graphs should have clear titles and labels while tables should have logical data placement. Text should concisely summarize results.

Best Practice in Data Management and Sharing Mojtaba Lotfaliany

It is about: Introduction: What Is “Research Data”? and Data Lifecycle Part 1: Why Manage Your Data? Formatting and organizing the data Storage and Security of Data Data documentation and meta data Quality Control Version controlling Working with sensitive data Controlled Vocabulary Centralized Data Management Part 2: Data sharing What are publishers & funders saying about data sharing? Researchers’ Attitudes Benefits of data sharing Considerations before data sharing Methods of Data Sharing Shared Data Uses and Its’ Limitations Data management plans Brief summary Acknowledgment , References

File_Organization_112014eshuppy

This document discusses best practices for organizing files and folders in research projects. It recommends following consistent naming conventions that provide context and description and adhering to a logical directory structure. Specifically, it suggests including relevant information like location, date, and version in file names. The document also stresses the importance of documentation and provides an example folder structure from the LOCI lab that organizes data by project, subproject, experiment, and replicates. Overall, the key aspects of file organization highlighted are naming conventions, directory structure, documentation, and consistency.

20170222 ku-librarians勉強会 #211 :海外研修報告：英国大学図書館を北から南へ巡る旅kulibrarians

Managing data throughout the research lifecycleMarieke Guy

This document summarizes a presentation about managing data throughout the research lifecycle. It discusses the stages of the research lifecycle, including planning, data creation, documentation, storage, sharing, and preservation. It provides examples of research lifecycle models and addresses key questions to consider at each stage, such as what formats to use, how to document data, where to store it, and how to share and preserve it. The presentation emphasizes making informed decisions about data management and talking to colleagues for support and advice.

Data Management for Graduate StudentsRebekah Cummings

This document provides an overview of data management best practices for graduate students presented in a workshop. It discusses what constitutes research data, the importance of managing data, how to create a data management plan, file naming conventions, metadata, data storage and backup strategies, and archiving options. The workshop covers topics like using a structured folder system, creating codebooks and documentation to describe data, and ensuring long-term access and preservation of research data. University librarians are available to help students with all aspects of responsible data management.

Data management (newest version)Graça Gabriel

This document provides guidance on managing research data. It discusses planning ahead by considering data needs, formats, volume and ethics. It also covers organizing data through file naming, metadata, references, remote access and safekeeping. Preserving data involves determining what to keep/delete and using long-term storage such as repositories. Reasons for sharing data include scientific integrity, funding mandates and increasing impact, while reasons for not sharing include financial or sensitive personal information.

Documentation and Metdata - VA DM BootcampSherry Lake

This document discusses documentation and metadata for research data. It begins with an overview of why documentation is important at different stages of the research data lifecycle from collection through archiving. Key elements to document include how the data was created, its content and structure, who created and maintains it, and how it can be accessed and cited. The document then discusses common documentation formats like readmes, data dictionaries, and codebooks. It also introduces metadata as structured information that describes resources and explains common metadata standards and tools for creating structured metadata files. Exercises guide creating documentation in these formats for a weather dataset example.

Managing Your Research DataKristin Briney

Introduction to Research Data Management for postgraduate studentsMarieke Guy

The document provides an introduction to research data management for postgraduate students, outlining what research data is, the research process, what research data management involves and why it is important, and how students can start thinking about good research data management practices. It discusses defining and organizing data, storage and security, and maintaining findable and understandable data throughout the research lifecycle. The goal is to explain the importance of research data management and the roles students play in effective data management.

Research Data Mangagement Essentials, 5th July 2017Research Data Leeds

FAIR BioData ManagementUlrike Wittig

The state of global research data initiatives: observations from a life on th...Projeto RCAAP

The document discusses research data management and provides guidance on best practices. It defines research data management as the active management of data over its lifecycle. It recommends writing a data management plan to document how data will be created, stored, shared, and preserved. It also provides tips for making data accessible and reusable through use of metadata standards, documentation, open licensing, and depositing data in repositories with persistent identifiers. The goal is to help researchers manage and share their data effectively to increase access and reuse.

My Dissertation Journeyjlposton

The document provides guidance for completing a dissertation journey, including: - Deciding between a qualitative or quantitative study approach, each with their own advantages and disadvantages. - Creating a proposal presentation for the dissertation committee on chapters 1-3 that includes frameworks, definitions, variables, and methodology. - Working with the research ethics committee and following submission guidelines. - Undergoing multiple revisions of all chapters based on committee feedback.

Best practices data collectionSherry Lake

Best practices data managementSherry Lake

Planning for Research Data Management: 26th January 2016IzzyChad

Take control of your PhD journey: Manage your research data according to best...Lars Figenschou

Data Archiving and SharingC. Tobin Magle

Planning for Research Data Managementdancrane_open

Data presentation and transferIyad Abou Rabii

Best Practice in Data Management and Sharing Mojtaba Lotfaliany

File_Organization_112014eshuppy

20170222 ku-librarians勉強会 #211 :海外研修報告：英国大学図書館を北から南へ巡る旅kulibrarians

Managing data throughout the research lifecycleMarieke Guy

Data Management for Graduate StudentsRebekah Cummings

Data management (newest version)Graça Gabriel

Documentation and Metdata - VA DM BootcampSherry Lake

Managing Your Research DataKristin Briney

Introduction to Research Data Management for postgraduate studentsMarieke Guy

Research Data Mangagement Essentials, 5th July 2017Research Data Leeds

FAIR BioData ManagementUlrike Wittig

The state of global research data initiatives: observations from a life on th...Projeto RCAAP

My Dissertation Journeyjlposton

Recently uploaded (20)

Parallel resonance circuits of science.pdfrk5867336912

UNIT chromatography instrumental6 .pptxmyselfit143

Skin function_protective_absorptive_Presentatation.pptxmuralinath2

Water analysis practical for ph, tds, hardness, acidity, conductivity, and ba...ss0077014

Examining Visual Attention in Gaze-Driven VR Learning: An Eye-Tracking Study ...Yasasi Abeysinghe

This study presents an eye-tracking user study for analyzing visual attention in a gaze-driven VR learning environment using a consumer-grade Meta Quest Pro VR headset. Eye tracking data were captured through the headset's built-in eye tracker. We then generated basic and advanced eye-tracking measures—such as fixation duration, saccade amplitude, and the ambient/focal attention coefficient K—as indicators of visual attention within the VR setting. The generated gaze data are visualized in an advanced gaze analytics dashboard, enabling us to assess users' gaze behaviors and attention during interactive VR learning tasks. This study contributes by proposing a novel approach for integrating advanced eye-tracking technology into VR learning environments, specifically utilizing consumer-grade head-mounted displays.

Chromatography, types, techniques, ppt.pptxDr Showkat Ahmad Wani

RAPID DIAGNOSTIC TEST (RDT) overviewppt.pptxnietakam

Introduction to Mobile Forensics Part 1.pptxNivya George

Chapter 4_Part 2_Infection and Immunity.pptJessaBalanggoyPagula

Turkey Diseases and Disorders Volume 2 Infectious and Nutritional Diseases, D...Ali Raei

Infrastructure for Tracking Information Flow from Social Media to U.S. TV New...Himarsha Jayanetti

This study examines the intersection between social media and mainstream television (TV) news with an aim to understand how social media content amplifies its impact through TV broadcasts. While many studies emphasize social media as a primary platform for information dissemination, they often underestimate its total influence by focusing solely on interactions within the platform. This research examines instances where social media posts gain prominence on TV broadcasts, reaching new audiences and prompting public discourse. By using TV news closed captions, on-screen text recognition, and social media logo detection, we analyze how social media is referenced in TV news.

06-Molecular basis of transformation.pptxLanaQadumii

Nutritional Diseases in poultry.........Bangladesh Agricultural University,Mymemsingh

Poultry require at least 38 dietary nutrients inappropriate concentrations for a balanced diet. A nutritional deficiency may be due to a nutrient being omitted from the diet, adverse interaction between nutrients in otherwise apparently well-fortified diets, or the overriding effect of specific anti-nutritional factors. Major components of foods are – Protein, Fats, Carbohydrates, Minerals, Vitamins Vitamins are A- Fat soluble vitamins: A, D, E, and K ; B - Water soluble vitamins: Thiamin (B1), Riboflavin (B2), Nicotinic acid (niacin), Pantothenic acid (B5), Biotin, folic acid, pyriodxin and cholin. Causes: Low levels of vitamin A in the feed. oxidation of vitamin A in the feed, errors in mixing and inter current disease, e.g. coccidiosis , worm infestation Clinical signs: Lacrimation (ocular discharge), White cheesy exudates under the eyelids (conjunctivitis). Sticky of eyelids and (xerophthalmia). Keratoconjunctivitis. Watery discharge from the nostrils. Sinusitis. Gasping and sneezing. Lack of yellow pigments, Respiratory sings due to affection of epithelium of the respiratory tract. Lesions: Pseudo diphtheritic membrane in digestive and respiratory system (Keratinized epithelia). Nutritional roup: respiratory sings due to affection of epithelium of the respiratory tract. Pustule like nodules in the upper digestive tract (buccal cavity, pharynx, esophagus). The urate deposits may be found on other visceral organs Treatment: Administer 3-5 times the recommended levels of vitamin A @ 10000 IU/ KG ration either through water or feed. Lesions: Pseudo diphtheritic membrane in digestive and respiratory system (Keratinized epithelia). Nutritional roup: respiratory sings due to affection of epithelium of the respiratory tract. Pustule like nodules in the upper digestive tract (buccal cavity, pharynx, esophagus). The urate deposits may be found on other visceral organs Treatment: Administer 3-5 times the recommended levels of vitamin A @ 10000 IU/ KG ration either through water or feed. Lesions: Pseudo diphtheritic membrane in digestive and respiratory system (Keratinized epithelia). Nutritional roup: respiratory sings due to affection of epithelium of the respiratory tract. Pustule like nodules in the upper digestive tract (buccal cavity, pharynx, esophagus). The urate deposits may be found on other visceral organs Treatment: Administer 3-5 times the recommended levels of vitamin A @ 10000 IU/ KG ration either through water or feed.

Causes of mortalities of eggs and spawn and remedies.pptxanshumanmohanty9090

Gel Electrophorosis, A Practical Lecture.pptxDr Showkat Ahmad Wani

Lecture 12 Types of farming systemNickala1

Influenza-Understanding-the-Deadly-Virus.pptxdiyapadhiyar

Metallurgical process class 11_Govinda PathakGovindaPathak6

Skin_Glands_Structure_Secretion _Controlmuralinath2

when is CT scan need in breast cancer patient.pptxRukhnuddin Al-daudar

Parallel resonance circuits of science.pdfrk5867336912

UNIT chromatography instrumental6 .pptxmyselfit143

Skin function_protective_absorptive_Presentatation.pptxmuralinath2

Water analysis practical for ph, tds, hardness, acidity, conductivity, and ba...ss0077014

Examining Visual Attention in Gaze-Driven VR Learning: An Eye-Tracking Study ...Yasasi Abeysinghe

Chromatography, types, techniques, ppt.pptxDr Showkat Ahmad Wani

RAPID DIAGNOSTIC TEST (RDT) overviewppt.pptxnietakam

Introduction to Mobile Forensics Part 1.pptxNivya George

Chapter 4_Part 2_Infection and Immunity.pptJessaBalanggoyPagula

Turkey Diseases and Disorders Volume 2 Infectious and Nutritional Diseases, D...Ali Raei

Infrastructure for Tracking Information Flow from Social Media to U.S. TV New...Himarsha Jayanetti

06-Molecular basis of transformation.pptxLanaQadumii

Nutritional Diseases in poultry.........Bangladesh Agricultural University,Mymemsingh

Causes of mortalities of eggs and spawn and remedies.pptxanshumanmohanty9090

Gel Electrophorosis, A Practical Lecture.pptxDr Showkat Ahmad Wani

Lecture 12 Types of farming systemNickala1

Influenza-Understanding-the-Deadly-Virus.pptxdiyapadhiyar

Metallurgical process class 11_Govinda PathakGovindaPathak6

Skin_Glands_Structure_Secretion _Controlmuralinath2

when is CT scan need in breast cancer patient.pptxRukhnuddin Al-daudar

Making your data good enough for sharing.

1. On Golf and Data Wolfgang Müller Making your data good enough for sharing

2. Challenge of data sharing • Most data never gets shared – Wrong experimental method – Hidden parameter discovered – Faulty experiment • How to prepare data in this situation? – Don‘t want to waste time – Want to be prepared if we share • Propose useful way forward

4. 80-20 rule Voltaire: „The best is the enemy of the good“ 80-20 rule: Often you can get 80% of the benefits using 20% of the effort. Tee-off Approach Put- ting Biggest approach in one shot

5. What to share? • Raw data (sometimes) • Condensed, interpreted data • Metadata: Data about the data – Conditions of the measurements – Information about the samples • What was sampled? • How was it prepared? • How was it treated after sampling?

6. Levels of detail • Action guidelines (e.g. SOP) • Structure guidelines (e.g. F1000 data preparation guidelines) • Semantics guidelines (metadata + content, e.g. some MIBBIs) • File format standards (e.g. ISA-TAB, SBML) • Ontologies + vocabularies (e.g. ChEBI)

7. Standardisation scales • Self • Group • Collaborative project • Field scale Increasedusabilityforothers

8. Self-standardisation • Store same things in same structure – Test question: „Does Excel cell (e.g.) A2 have the same meaning in all files about the same experiment type“? • Name same things the same way – Test question: „Does ‚gl‘ mean exactly the same in all occurences“? • Identify uniquely things that you reference. Benefit: Automatic adaptation of your data much easier

9. Identify uniquely (e.g. McCurry et al. preprint) 1. If you create identifiers, do not DIY (Do Identifiers by Yourself) 2. Help identifiers travel well: don’t let them leave home without a Prefix and a Namespace 3. Make Local Resource Identifiers rugged to realworld use 4. Make the full URI simple and durable 5. Carefully consider whether to embed meaning 6. Make the full URI and CURIE clear and easy to find 7. Implement a version management policy 8. Manage complex lifecycles without deletion 9. Document the identifiers you issue and use 10. Reference responsibly and rely on full URIs

10. Standardisation within group or project Same as before, but in addition: • Needs agreeing on how to do things the same way • Needs looking into standards for your domain – Inspiration how to proceed – Clear insight into migration paths

11. e.g. F1000 data preparation guidelines • Give each column a descriptive heading • Use a single header row • Ensure you have used the first cell, i.e. A1 • Include Title & Legend for each spreadsheet • Save each data file with a telling name • Submit each table as a separate file • Submit each work sheet as a separate file

12. JERM templates

13. Systems Biology Markup Language • XML-Based format – Levels and Versions – Packages • Model of relations within SBML files as UML • Library implementations • MIRIAM guidelines for proper annotation of SBML files • MIRIAM resources, MIRIAM resolver for providing identifiers and links • ...

14. biosharing.org

15. Modify reproducibly

Making your data good enough for sharing.

Recommended

More Related Content

What's hot (20)

Viewers also liked (7)

Similar to Making your data good enough for sharing. (20)

Recently uploaded (20)

Making your data good enough for sharing.