Data Management in the context of Open Science.
Because open access become mandatory for publications and project-funded research data, it is the responsibility of each researcher to be informed and then trained in new practices.
Good (enough) research data management practicesLeon Osinski
Slides of a lecture on research data management (RDM), given for 3rd year students (Eindhoven University of Technology, major Psychology & Technology), as part of the course 0HV90 Quantitative Research. At the end of the slides a handy summary 'Research data management basics in a nutshell' is added.
What funders want you to do with your dataLeon Osinski
Funders want researchers to 1) deposit the relevant data from their research in an approved repository to make it FAIR (Findable, Accessible, Interoperable, Reusable), 2) make the data openly available whenever possible, and 3) write a Data Management Plan describing how they will manage their data during and after the project. Funders require depositing data in repositories to enable reuse, making data open access "as open as possible, as closed as necessary", and having a Data Management Plan that addresses reuse according to FAIR principles.
A basic course on Research data management: part 1 - part 4Leon Osinski
Slides belonging to a basic course on research data management. The course consists of 4 parts:
Part 1: what and why
1.1 data management plans
Part 2: protecting and organizing your data
2.1 data safety and data security
2.2 file naming, organizing data (TIER documentation protocol)
Part 3: sharing your data
3.1 via collaboration platforms (during research)
3.2 via data archives (after your research)
Part 4: caring for your data, or making data usable
4.1 tidy data
4.2 documentation/metadata
4.3 licenses
4.4 open data formats
Research data management at TU EindhovenLeon Osinski
The document discusses research data management at TU Eindhoven. It outlines the long process of developing RDM practices since 2008. It describes the current organization and governance structure for RDM. Key external requirements for RDM from funders, regulations, and integrity standards are also summarized. The document concludes by outlining RDM support services available and the benefits of good RDM practices.
A basic course on Research data management, part 1: what and whyLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A basic course on Research data management, part 4: caring for your data, or ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
S. Venkataraman (DCC) talks about the basics of Research Data Management and how to apply this when creating or reviewing a Data Management Plan (DMP). He discusses data formats and metadata standards, persistent identifiers, licensing, controlled vocabularies and data repositories.
link to : dcc.ac.uk/resources
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.
Good (enough) research data management practicesLeon Osinski
Slides of a lecture on research data management (RDM), given for 3rd year students (Eindhoven University of Technology, major Psychology & Technology), as part of the course 0HV90 Quantitative Research. At the end of the slides a handy summary 'Research data management basics in a nutshell' is added.
What funders want you to do with your dataLeon Osinski
Funders want researchers to 1) deposit the relevant data from their research in an approved repository to make it FAIR (Findable, Accessible, Interoperable, Reusable), 2) make the data openly available whenever possible, and 3) write a Data Management Plan describing how they will manage their data during and after the project. Funders require depositing data in repositories to enable reuse, making data open access "as open as possible, as closed as necessary", and having a Data Management Plan that addresses reuse according to FAIR principles.
A basic course on Research data management: part 1 - part 4Leon Osinski
Slides belonging to a basic course on research data management. The course consists of 4 parts:
Part 1: what and why
1.1 data management plans
Part 2: protecting and organizing your data
2.1 data safety and data security
2.2 file naming, organizing data (TIER documentation protocol)
Part 3: sharing your data
3.1 via collaboration platforms (during research)
3.2 via data archives (after your research)
Part 4: caring for your data, or making data usable
4.1 tidy data
4.2 documentation/metadata
4.3 licenses
4.4 open data formats
Research data management at TU EindhovenLeon Osinski
The document discusses research data management at TU Eindhoven. It outlines the long process of developing RDM practices since 2008. It describes the current organization and governance structure for RDM. Key external requirements for RDM from funders, regulations, and integrity standards are also summarized. The document concludes by outlining RDM support services available and the benefits of good RDM practices.
A basic course on Research data management, part 1: what and whyLeon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
A basic course on Research data management, part 4: caring for your data, or ...Leon Osinski
A basic course on research data management for PhD students. The course consists of 4 parts. The course was given at Eindhoven University of Technology (TUe), 24-01-2017
S. Venkataraman (DCC) talks about the basics of Research Data Management and how to apply this when creating or reviewing a Data Management Plan (DMP). He discusses data formats and metadata standards, persistent identifiers, licensing, controlled vocabularies and data repositories.
link to : dcc.ac.uk/resources
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.
This document provides an introduction to data management. It discusses why data management is important, covering key aspects like developing data management plans, file organization, documentation and metadata, storage and backup, legal and ethical considerations, sharing and reuse, and preservation. Effective data management is critical for research success as it supports reproducibility, sharing, and preventing data loss. The document outlines best practices and resources like the library that can help with developing strong data management strategies.
David Shotton - Research Integrity: Integrity of the published recordJisc
The document discusses several issues related to publishing research data and proposes solutions to address them. It describes projects that aim to make it easier for researchers to publish, archive, cite and reuse research data. This includes developing metadata standards, data repositories, and publishing data citations as linked open data to improve data discovery and attribution.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This presentation introduces the basics of the Dataverse including preparing the submission to the Dataverse, creating an account and logging in, adding datasets to the Dataverse account, and metadata.
This presentation gives an overview of the key things that we need to consider before deciding to set up a data repository. It briefly talks about data repository, the software behind data repository and their limitations and merits. Additionally, the presenters shared IFPRI's experiences with Harvard Dataverse.
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
This slideshow was used at a lunchtime session delivered at the Humanities Division, University of Oxford, on 2014-05-12. It provides a general overview of some key data management topics, plus some pointers on where to find further information.
Data Literacy: Creating and Managing Reserach Datacunera
This document discusses best practices for creating and managing research data. It covers defining data, the importance of data management, developing a data management plan, file naming conventions, metadata, data sharing and preservation. Key points include making a data management plan addressing types of data, standards, access and sharing policies; using descriptive file names with dates; storing multiple versions of data; and including metadata to explain the data. Resources for data management support are provided.
This document provides an introduction to data management. It discusses the importance of data management and introduces best practices. These include making a data management plan, properly organizing and naming files, adding descriptive metadata, securely storing and backing up data, considering legal and ethical issues, enabling sharing and reuse, and ensuring long-term preservation. Effective data management is important across all disciplines and throughout the entire data lifecycle from creation to archiving.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Presentation for Northwestern University's first Computational Research Day, April 22, 2014. http://www.it.northwestern.edu/research/about/campus-events/research-day/agenda.html . By Cunera Buys, e-Science Librarian, and Claire Stewart, Director, Center for Scholarly Communication and Digital Curation and Head, Digital Collections
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.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2015-02-09. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
DataONE Education Module 03: Data Management PlanningDataONE
Lesson 3 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This document summarizes requirements for sharing research data and articles that have been established by federal agencies. It outlines policies from the NIH, NSF, DOE and other organizations regarding depositing publications in repositories like PMC and making underlying data publicly available. Agencies vary in the required timeframe for sharing articles and data, from the time of publication to 12-30 months later. The document also reviews benefits to researchers of managing and sharing data, journal data sharing policies, and resources for data management at Northwestern University Library.
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
The document provides an overview of the Donders Repository, which aims to securely store original research data, document the research process, and make data accessible to researchers and the public. It describes the procedural design including different roles, collection types, and states. The technical architecture is based on IRODS software and scalable storage. The repository fits into researchers' workflows and supports the timeline of projects from initiation to data sharing. Standards like BIDS help make neuroimaging data FAIR (Findable, Accessible, Interoperable, Reusable).
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
The FOSTER project aims to support stakeholders, especially young researchers, in adopting open access practices that comply with Horizon 2020 requirements. It will develop training materials and an e-learning portal, deliver face-to-face training for trainers, and help institutions strengthen their open access training capacity. The project seeks to facilitate adoption of open access policies across European funders in line with the EC's recommendation and support the transition to open science.
This document provides an introduction to data management. It discusses why data management is important, covering key aspects like developing data management plans, file organization, documentation and metadata, storage and backup, legal and ethical considerations, sharing and reuse, and preservation. Effective data management is critical for research success as it supports reproducibility, sharing, and preventing data loss. The document outlines best practices and resources like the library that can help with developing strong data management strategies.
David Shotton - Research Integrity: Integrity of the published recordJisc
The document discusses several issues related to publishing research data and proposes solutions to address them. It describes projects that aim to make it easier for researchers to publish, archive, cite and reuse research data. This includes developing metadata standards, data repositories, and publishing data citations as linked open data to improve data discovery and attribution.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This presentation introduces the basics of the Dataverse including preparing the submission to the Dataverse, creating an account and logging in, adding datasets to the Dataverse account, and metadata.
This presentation gives an overview of the key things that we need to consider before deciding to set up a data repository. It briefly talks about data repository, the software behind data repository and their limitations and merits. Additionally, the presenters shared IFPRI's experiences with Harvard Dataverse.
This presentation gives an overview of the key things that we need to consider before publishing data from the repository. It briefly discusses research data management, research data lifecycle, FAIR principles of research data management and then move on to key elements that should be considered while preparing datasets for publishing through repository.
This slideshow was used at a lunchtime session delivered at the Humanities Division, University of Oxford, on 2014-05-12. It provides a general overview of some key data management topics, plus some pointers on where to find further information.
Data Literacy: Creating and Managing Reserach Datacunera
This document discusses best practices for creating and managing research data. It covers defining data, the importance of data management, developing a data management plan, file naming conventions, metadata, data sharing and preservation. Key points include making a data management plan addressing types of data, standards, access and sharing policies; using descriptive file names with dates; storing multiple versions of data; and including metadata to explain the data. Resources for data management support are provided.
This document provides an introduction to data management. It discusses the importance of data management and introduces best practices. These include making a data management plan, properly organizing and naming files, adding descriptive metadata, securely storing and backing up data, considering legal and ethical issues, enabling sharing and reuse, and ensuring long-term preservation. Effective data management is important across all disciplines and throughout the entire data lifecycle from creation to archiving.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Presentation for Northwestern University's first Computational Research Day, April 22, 2014. http://www.it.northwestern.edu/research/about/campus-events/research-day/agenda.html . By Cunera Buys, e-Science Librarian, and Claire Stewart, Director, Center for Scholarly Communication and Digital Curation and Head, Digital Collections
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.
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2015-02-09. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
DataONE Education Module 03: Data Management PlanningDataONE
Lesson 3 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This document summarizes requirements for sharing research data and articles that have been established by federal agencies. It outlines policies from the NIH, NSF, DOE and other organizations regarding depositing publications in repositories like PMC and making underlying data publicly available. Agencies vary in the required timeframe for sharing articles and data, from the time of publication to 12-30 months later. The document also reviews benefits to researchers of managing and sharing data, journal data sharing policies, and resources for data management at Northwestern University Library.
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
The document provides an overview of the Donders Repository, which aims to securely store original research data, document the research process, and make data accessible to researchers and the public. It describes the procedural design including different roles, collection types, and states. The technical architecture is based on IRODS software and scalable storage. The repository fits into researchers' workflows and supports the timeline of projects from initiation to data sharing. Standards like BIDS help make neuroimaging data FAIR (Findable, Accessible, Interoperable, Reusable).
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
The FOSTER project aims to support stakeholders, especially young researchers, in adopting open access practices that comply with Horizon 2020 requirements. It will develop training materials and an e-learning portal, deliver face-to-face training for trainers, and help institutions strengthen their open access training capacity. The project seeks to facilitate adoption of open access policies across European funders in line with the EC's recommendation and support the transition to open science.
Research data management: a tale of two paradigms: Martin Donnelly
Presentation I was supposed to give at "Scotland’s Collections and the Digital Humanities" workshop in Edinburgh on May 2nd 2014. Illness prevented it, but my heroic DCC colleague Jonathan Rans stepped up and delivered the presentation on my behalf.
Research Data Management: A Tale of Two Paradigmstarastar
Presentation by Martin Donnelly, Digital Curation Centre, University of Edinburgh. Invited talk at a workshop for 'Scotland's National Collections and the Digital Humanities,' a knowledge-exchange project hosted at the University of Edinburgh. 2 May 2014. http://www.blogs.hss.ed.ac.uk/archives-now/
This document discusses open science and FAIR data principles. It begins by outlining the benefits of open data, including enabling reproducibility, avoiding replication gaps, and allowing data reuse and reinterpretation. Open data practices have transformed areas like genomics and astronomy. FAIR data principles help enable large-scale data use and machine analysis. The document then defines open science, including open access, open data, FAIR data principles, and engagement with society. It discusses frameworks for developing open data strategies at the national and institutional levels. These include developing policies, incentives, skills training, and data infrastructure. While open data brings benefits, it also requires investment and cultural changes to fully realize. Stakeholders like government and research institutions can benefit
I o dav data workshop prof wafula final 19.9.17Tom Nyongesa
The document summarizes an iODaV Data Workshop held at JKUAT in Kenya on open data and the JORD policy. It discusses why open data is important for reproducibility, innovation and scientific discovery. It outlines the FAIR principles for open data and metadata to make data findable, accessible, interoperable and reusable. It also discusses opportunities and challenges of open data for universities, including developing skills and infrastructure. Finally, it provides examples of open data initiatives at JKUAT including developing an open data policy, the iODaV program, contributions to national ICT policies, and the digital health applied research centre.
Open Data - strategies for research data management & impact of best practicesMartin Donnelly
This document summarizes a presentation on open data strategies and research data management best practices. It discusses the importance of open data as part of the broader open science movement. The presenter outlines good practices for research data management, including planning, documentation, storage, and deposition. Benefits of good research data management include increased impact, accessibility, transparency, efficiency and data durability. Risks of poor management include legal issues, financial penalties, lost scientific opportunities and reputational harm. The presentation provides a step-by-step approach to research data management and discusses roles and responsibilities of different stakeholders.
Mind the Gap: Reflections on Data Policies and PracticeLizLyon
UKOLN is supported by the Mind the Gap project which reflects on data policies and practices. The document discusses the current state of data practices in institutions, challenges around open science and data sharing, and the need for improved data policies, planning tools, and codes of conduct to help researchers with issues like data storage, sharing, and long-term preservation. It also explores how emerging technologies and areas like genomics, personalized medicine, and citizen science will impact future data practices and policies.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Research Data Management: a gentle introduction for admin staffMartin Donnelly
The document provides an overview of research data management (RDM) for administrative staff. It defines RDM as the active management of data over its lifecycle, and discusses why RDM is important due to funder requirements, risk management, and transparency. It outlines key roles and responsibilities for researchers and support staff, noting support staff should understand funder policies, provide guidance to researchers, and expect questions about RDM processes.
Museum collections as research data - October 2019Dag Endresen
This document discusses how natural history museums can embrace open science principles by making their collections openly available as research data. It provides context on initiatives like GBIF and DiSSCo that aim to publish biodiversity data according to common standards. While only around 5-10% of specimen records are currently digitized globally, the push for open access to publicly funded research means that museums need to develop new approaches to remain relevant providers of scientific resources. Open science practices like data sharing, citation and reuse can help address reproducibility issues and enable new discovery.
The State of Open Data Report by @figshare.
A selection of analyses and articles about open data, curated by Figshare
Foreword by Professor Sir Nigel Shadbolt
OCTOBER 2016
The document provides an overview of open science and its benefits. It discusses how open science involves making research outputs like publications and data openly accessible and reusable. Open access to publications and data sharing are required by Horizon 2020, the EU research funding program. It must be ensured that publications resulting from Horizon 2020 funding are made openly accessible within 6 months, and data must be deposited in repositories to validate results. Overall open science is aimed at increasing the benefits and impacts of research.
Open Data in a Big Data World: easy to say, but hard to do?LEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
This document summarizes a presentation on open science and open data. It discusses the importance of open research data for reproducibility and innovation. It outlines key policy developments promoting open data, including funder data policies and journal data policies. It also describes CODATA's activities related to data policies, frameworks for developing open data strategies, and components of the international open science ecosystem.
Martin Donnelly - Digital Data Curation at the Digital Curation Centre (DH2016)dri_ireland
Presentation given by Martin Donnelly, Senior Institutional Support Officer at the Digital Curation Centre (DCC), as part of the panel session “Digital data sharing: the opportunities and challenges of opening research” at the Digital Humanities conference, Krakow, 15 July 2016. The presentation looks at digital data curation at the DCC.
The document discusses policy, infrastructure, skills, and incentives related to data sharing in Africa. It provides information about the University of Botswana, including its faculties, research centers, and digital repository. It then discusses the upcoming International Data Week conference in Gaborone, Botswana, and themes related to digital science such as open data, data analysis, and data stewardship. Finally, it summarizes the proposed African Open Science Platform project to coordinate open science activities across Africa through a centralized initiative.
Metadata management for data storage spaces :
INDEXATOR is a metadata management tool that addresses the problems of organising, documenting, storing and sharing data in a research unit or infrastructure, and fits perfectly into a data management plan of a collective.
The central idea is that the storage space becomes the data repository, so the metadata should go to the data and not the other way around.
Given the diversity of domains, the approach chosen is to be both as flexible and as pragmatic as possible by allowing each collective to choose its own (controlled) vocabulary corresponding to the reality of its field and activities. The main idea is to be able to "capture" the user's metadata as easily as possible using their vocabulary. It is possible to define the whole terminology using a spreadsheet.
The choice was made for the JSON format, which is very appropriate for describing metadata, readable by both humans and machines.
This tool is built around a web interface coupled with a MongoDB database. The web interface allows you to i) Describe a dataset using metadata of various types (Description), ii) Search datasets by their metadata (Accessibility).
How to best manage your data to make the most of it for your research - With ODAM Framework (Open Data for Access and Mining) Give an open access to your data and make them ready to be mined
BioStatFlow is a web application useful to analyze "OMICS", including metabolomics, data with statistical methods.
BioStatFlow is available online: http://biostatflow.org
ODAM is an Experiment Data Table Management System (EDTMS) that gives you an open access to your data and make them ready to be mined - A data explorer as bonus
Spectra processing is crucial in metabolomics approaches, especially for proton NMR metabolomic profiling, since each processing step may impact the following steps. Among the different processing steps, data reduction (binning or bucketing) strongly impacts subsequent statistical data analysis and potential biomarker discovery. Based on a recently published work, we propose an improved method of data reduction, called ERVA which stands for Extraction of Relevant Variables for Analysis. This new method, by providing buckets centred on resonance peaks and rid of any non-significant signal, helps to recover the chemical fingerprints of metabolites. Moreover, we take advantage of the concentration variability of each compound from a series of samples of a complex mixture, to highlight chemical information. This is performed by linking the buckets into clusters based on significant correlations, thus bringing a helpful support for compound identification. As a proof of concept, this new method has been applied to a tomato 1H-NMR dataset to test its ability to recover fruit extract composition.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
2. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 2
• Links between Research Data and Open Science
• How the management and preservation of Research Data
can facilitate the work of researchers
• How to address concerns about Data Sharing
• The research Data life cycle
At the end of the course you should understand...
3. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 3
The Reproducibility Crisis
In recent years, evidence has emerged from disciplines ranging from biology to
economics that many scientific studies are not reproducible.
This evidence has led to declarations in both the scientific and lay press that
science is experiencing a “reproducibility crisis” and that this crisis has
significant impacts on both science and society, including misdirected effort,
funding, and policy implemented on the basis of irreproducible research.
Franklin Sayre, Amy Riegelman (2018) C&RL 79(1) https://doi.org/10.5860/crl.79.1.2
4. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 4
This phenomenon appears, for example, in medicine, more precisely in
epidemiology, where, based on a large number of data (weight, age of the first
cigarette, etc.) and a large number of possible outcomes (breast cancer, lung
cancer, car accident, etc.), hazardous associations are made (a posteriori) and
statistically "validated".
p-hacking
p-hacking (also data dredging data fishing, data snooping, … ) is the misuse of
data analysis to find patterns in data that can be presented as statistically
significant when in fact there is no real underlying effect.
This is done by performing many statistical tests on the data and only paying
attention to those that come back with significant results, instead of stating a
single hypothesis about an underlying effect before the analysis and then
conducting a single test for it
https://en.wikipedia.org/wiki/Data_dredging
5. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 5
Cholesterol and Controversy: Past, present and Future
By Jeanne Garbarino on November 15, 2011
Scientific American - Blog
https://blogs.scientificamerican.com/guest-blog/cholesterol-
confusion-and-why-we-should-rethink-our-approach-to-statin-
therapy/
Cholesterol controversy
The French paradox: lessons for other countries
Heart. 2004 Jan; 90(1): 107–111.
doi: 10.1136/heart.90.1.107
Jean Ferrières
Plot of death rate from coronary heart disease (1977)
correlated with daily dietary intake (from 1976 to 1978) of
cholesterol and saturated fat as expressed by the
cholesterol fat index (CSI) per 1000 kcal
Correlation does not mean causal relationship !
6. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 6
Open Science
7. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
DATA Studies
Research Project
During a research project
Know-how knowledge
Input Output
7
8. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
What do they become?
• Nothing ! They rest on a disk space (up to its death!)
Among the possible scenarios, two of them are extreme
• Creation of a comprehensive database managing all
data and metadata in its entirety, associated with a
visualization and querying interface.
Expected objectives
After the project is completed
DATA Studies
8
Research Project
9. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
Expected objectives
Scientific Data Repositories
Enrichment
Expected links
DATA Studies
Publishing policies
…
9
https://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-dissemination_en.htm
Research Project
10. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
NATIONAL PLAN FOR OPEN SCIENCE
Open science is the practice of making research publications and
data freely available (transparency)
Open science seeks to create an ecosystem in which scientific
research is more cumulative (interdisciplinary)
Open science makes knowledge accessible to all (civic aspect)
Open science also drives scientific progress (reactivity)
Finally, open science fosters scientific integrity and people’s trust
in science (ethics)
http://cache.media.enseignementsup-recherche.gouv.fr/file/Recherche/50/1/SO_A4_2018_EN_01_leger_982501.pdf
announced by Frédérique Vidal on 4 July 2018
makes open access mandatory for publications and project-funded research data.
10
12. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 12
Interdisciplinary
Data
Science
Scientific
Field
IT
Skills
Data Management
Data InterpretationData Analysis
Open Science is a new research paradigm facing many challenges, mainly :
Requirement of many skills
the ingrained research habits
Statistics
Software Data
13. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
Science today - context
Knowledge creation
Experimental science
Theoretical science
Data-intensive science /
Data-driven science
Requires three skills:
Scientific field
Information management
Data processing
Research Paradigms
What are the
consequences on the
data?
Publications + Data
Not only induction, deduction
but above all abduction >> data science
New Paradigm
13
14. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 14
Abduction
Abduction is a type of reasoning consisting in inferring probable causes to
an observed fact.
In other words, it is a question of establishing a most probable cause of a
fact found …
… and stating, as a hypothesis, that the fact in question probably results
from that cause.
Data Science
Data-driven science
15. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
Data from observation, experimentation or derived from existing sources
that are analyzed in order to produce or validate research results original
What is the Research Data ?
Digital Data Tables, Text Files, Sound Recordings, Completed
Survey Questionnaires, Image or Video Database, Derived data or
compiled
“Data, or units of information, related to research activities, whether funded or
not, are often organized or formatted in such a way that they can be
communicated, interpreted and processed. Research Data are all the information
you use as part of your research “ according to the University of Bristol
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16. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 16
“Data management should be woven into every course in science.”
Data's shameful neglect
Nature 461, 2009 (Editorial)
orchestrates data for efficient and reliable use
increases the impact of research,
improves the visibility of research
allows data to be shared securely
makes it easy to find the data
reduces the risk of data loss
increases citation rates
requirement of most funders and publishers
RDM benefits
Data Management Facilitates
Sharing and Re-use …
Why do we have to "manage" the Research Data
based on the Open Science paradigm ?
https://www.nature.com/articles/461145a
17. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
• Primary/secondary
• Experimental, observational, simulation, derived, compiled, canonical
• Raw, processed, aggregated, enriched, annotated, formatted, standardized, processed,
published
• Structured/unstructured, homogenous/heterogeneous
• Free / protected
Manage?... but manage what?
17
18. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 18
Data
Creation
Data
processing
Data
Analysis
Data
preservation
Data
dissemination
Re-Use
Data
Collection: experiments, measurements,
observations, simulations
Creation
of metadata
Enter, format, clean,
organize, verify, validate,
describe, store
Interpretation, visualization,
formatting, publication
Migration, reformatting,
back-up, permanent storage,
Metadata, documentation, certification
Distribution, referencing,
Reporting, rights management
Data journals
Teaching,
new research,
evaluation
Curation
of data
The data life cycle
Integrate scientific data
management into research
activities
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IT Manager / System Administrator
«skilled partner» in data archiving and
preservation
Data Creator
people who produce digital data
Data Manager
expert on the management, reporting,
storage and dissemination of research data
Data Scientist
data analysis
A wide variety of fields
Rapid developments - Continuing training required
New jobs require more and more IT skills
Research Data Management
Support - skills and professions
The data life cycle
at each stage, services can be developed:
- development of Data Management Plan (DMP)
- identification of metadata describing the data
- selection of warehouses to store data
- data retention infrastructures
- data discovery and mining tools
- data reuse framework
The scientific data life cycle is the set of
stages of management, conservation,
dissemination and reuse of scientific
data related to research activities.
19
https://ec.europa.eu/research/openscience/pdf/os_skills_wgreport_final.pdf
20. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 https://www6.inra.fr/datapartage/
A data management plan or DMP is a formal document that outlines
how data will be obtained, processed, organized, stored, secured, preserved, shared
both during a research project, and after the project is completed.
The goal of a data management plan is to consider
the many aspects of data management, metadata generation, data preservation, and analysis
before the project begins
this ensures that data are well-managed
in the present, and prepared for preservation in the future.
Optimization of Data Sharing and
Interoperability of Research
https://dmp.opidor.fr/
Main step of data management
Tool to be used as soon as projects are set up
Data Management Plan (DMP)
20
21. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 21
Operational DetailsData Management Plan (DMP)
22. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 22
How does the
management of data is
it funded, especially in
the long term?
Resources
What does the project consist of?
Who are the partners?
What policy on data management?
Who is responsible for the
management of data?
Responsibilities
in the project
What data will be produced/used
during the course of the project
(type, format, volume and
increase...) ?
How will they be produced?
processed?
Data collection
How, where, where, by
whom, will be stored,
backed up and secured
the data?
Data backup
Data Management Plan (DMP)
Who will be able to access the
data? The data will they be shared?
published? With whom? How?
How long does it take? Under which
license?
Data Access and Data sharing
Who will own it?
of the data produced
External data
will they be used?
Intellectual Property
What is the plan for
long-term archiving and
preservation?
Data Archiving
How will the data be identified,
described? What metadata
standards will be used?
How will the metadata be
generated?
Data Documentation
23. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
Findable Accessible
Interoperable Reusable
Describe your data in a data repository
Apply a persistent identifier
Consider what will be shared
Obtain participant consent
Use open formats
Consistent vocabulary
Common metadata standards
Consider permitted use
Apply appropriate license
23
The FAIR Data Principles are a set of guiding principles to make data accessible, interoperable and
reusable (Wilkinson et al.,2016 Scientific Data - https://www.nature.com/articles/sdata201618).
https://www.force11.org/group/fairgroup/fairprinciples
RDM based on the Open Science : THE FAIR DATA PRINCIPLES
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THE FAIR DATA PRINCIPLES
A1.2 => Open as much as possible, Close as much as necessary
25. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 25
THE FAIR DATA PRINCIPLES
5 ★ OPEN DATA
26. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 26
It is above all an approach to measure
the maturity of your data in relation to
Open DATA
THE FAIR DATA PRINCIPLES
https://www.go-fair.org/
From Principles towards Implementations
The Internet of FAIR Data & Services
27. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 27
DMP model H2020 based on FAIR principles
https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
Guidelines on FAIR Data Management in Horizon 2020
1. Data Summary
2. FAIR data
2.1. Making data findable, including provisions for metadata
2.2. Making data openly accessible
2.3. Making data interoperable
2.4. Increase data re-use (through clarifying licences)
3. Allocation of resources
4. Data security
5. Ethical aspects
6. Other issues
7. Further support in developing your DMP
28. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
Data on the web, open license
… in a structured format
… and non-proprietary format
… identified by URIs
… and related to others (data)
5 ★ OPEN DATA
Publish data "5 Gold stars"
Tim Berners-Lee, the inventor of the Web and Linked Data
initiator, suggested a 5-star deployment scheme for Open Data
28
K. Janowicz et al (2014) Five Stars of Linked Data Vocabulary Use
Semantic Web 0 (2014) 1–0
https://geog.ucsb.edu/~jano/swj653.pdf
See also
29. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
SERVICE DESCRIPTION
re3data is a global registry of research data repositories from a diverse range of academic disciplines.
It provides information on repositories for the permanent storage and access of data sets to
researchers, funding bodies, publishers and scholarly institutions.
Research Data Repositories are based on
web applications to preserve, share, cite, search and analyse research data.
…
https://data.inra.fr/
Science Europe’s Framework for Discipline-specific
Research Data Management
29
https://www.nature.com/sdata/policies/repositories
Recommended Data Repositories
https://fairsharing.org/databases/
30. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 30
https://data.inra.fr/
31. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 31
…
2,406 Data Repositories (Oct 10, 2019)
https://www.re3data.org/metrics
Not FAIR !!
FAIR ?
32. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 32
Reproducible Research
in the context of Open Science
33. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 33
Some issues often arise with users jumping straight into software implementations of
methods (e.g. in R) that may lack documentation on biases and assumptions that are
mentioned in the original papers.
Halsey et al (2015) The fickle P value generates irreproducible results, Nature Methods 12, 179–185
Calls for Open Science & Reproducible Research
Typical examples of where problems can arise
A major cause of lack of repeatability (often not being considered) is the wide sample-
to-sample variability in the P value. Due to that p-value is fickle, the interpreting of
analyses should not be based predominantly on this statistic.
Overfitting a model is a condition where a statistical model begins to describe the
random error in the data rather than the relationships between variables. This
problem occurs when the model is too complex. In regression analysis, overfitting
can produce misleading R-squared values, regression coefficients, and p-values.
https://statisticsbyjim.com/regression/overfitting-regression-models/
34. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 34
Calls for Open Science & Reproducible Research
Others issues
Loss of data and/or information :
Not regularly backing up your data is considered as professional negligence
Lack of knowledge, lack of technical skills, having more or less hazardous practices :
Training is a right but also a duty to claim to fully assume a function / mission
Continuous evolution of software libraries & their dependencies
Problems related to digital accuracy from one computer to another,
Versioning,
…
Miscellaneous
35. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 35
“Citations to unpublished data and personal communications
cannot be used to support claims in a published paper”
“All data necessary to understand, assess, and extend the
conclusions of the manuscript must be available to any reader
of science.
What Science Requires
Calls for Open Science & Reproducible Research
36. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 36
Research is defined as reproducible when then published results
can be replicated using the documented data, code, and methods
employed by the author or provider without the need for any
additional information or needing to communicate with the author
or provider
Reproducible Research
https://nnlm.gov/data/thesaurus/reproducible-research
Reproducible research is
is not a guarantee of research quality, but a guarantee of transparency.
contributes to quality but does not replace it
37. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 37
Reproducibility has the potential to serve as a minimum standard for judging scientific
claims when full independent replication of a study is not possible
Reproducible Research
38. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 38
Reproducible Research
Good practices
Data Collection and Management :
Write an information collection protocol: this protocol should be part of the published article
Maintain a laboratory notebook
Collect data repeatedly AND reproducibly
Research Compendium :
facilitates reproducible research by bringing together in a single
virtual "place" the data, codes, protocols and documentation
related to a research project
Full computational environment used to produce the results in the
paper such as the code, data, etc. that can be used to reproduce
the results and create new work based on the research.
39. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 39
Reproducible Research
Good practices
Manage what ? What kind of data/information ?
The minimal but mandatory set of files
From RAW DATA To Final results
Including
• Standard Operating Procedures (SOP)
• Data reporting
Checking
Validation
Tracing
Raw Data
Processed
data
40. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 40
Reproducible Research
Good practices
The minimal but mandatory set of files
Checking
Validation
Tracing
The final
quantification
results file
The calibration file
(Calibration curves based on
standard compounds)
The Excel worksheet(s)
having served to calculate
the quantification
The compound
attribution zones
An image of an annotated
NMR spectrum
Protocol documents that describe each step of the process (Quality Assurance):
I. Analytical sample preparation
II. Analytical processing
III. Data processing
IV. Quantification
The raw
NMR
spectra
(ZIP file)
Example: 1H-NMR Analytical Technique
http://nmrprocflow.org/ex1
Example of full 1H-NMR data set
Manage what ? What kind of data/information ?
41. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 41
Reproducible Research
Good practices
Backups :
Not regularly backing up your data is considered as professional negligence
Versions and Archives :
Safeguarding the successive stages of document development (texts, data, codes, etc.) is one of
the fundamental building blocks of reproducible research
Implementation of a version management strategy
Git + local or institutional Forge (i.e. Forgemia), GitHub (i.e. github/INRA)
Research data repositories (re3data.org)
42. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 42
Reproducible Research
Good advices
Data exploration
Use tools that you know well or that allow you to gain in efficiency.
But
Learn to program :
Limit the use of graphical interfaces (GUI) for subtle or repetitive tasks
Be able to express in a clear, documented and unambiguous way what you want the software to do
A program can be simply expressed in a few lines only. The higher the level of language used, the less
there will be to write.
Typical examples of reproducible research comprise compendia of data, code and text files, often
organised around an R Markdown source document or a Jupyter notebook.
43. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 43
Open Data for Access and Mining
ODAM Framework
Example of a Data Management System in the context of Open Science
http://pmb-bordeaux.fr/dataexplorer/
http://pmb-bordeaux.fr/odam/FAIR_and_DataLife_DJ_Oct2019.pdf
https://nbviewer.jupyter.org/github/djacob65/binder_odam/blob/master/PyODAM_api_PCA.ipynb
44. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019
https://doranum.fr/
Research Data - Digital Learning
https://coop-ist.cirad.fr/gerer-des-donnees
CoopIST – Cooperate in Scientific and Technical Information
INRA services and resources
https://www6.inra.fr/datapartage
Some useful links related to Open Science / Data Management
The future of science is Open
https://www.fosteropenscience.eu/
Building the social and technical bridges to enable open sharing and re-use of data
https://www.rd-alliance.org/ 23 Things: Libraries for Research Data
44
45. Daniel Jacob – INRA UMR 1332 BFP – Oct 2019 45
Vers une recherche reproductible : Faire évoluer ses pratiques
https://hal.archives-ouvertes.fr/hal-02144142v1
https://englianhu.files.wordpress.com/2016/01/reproducible-research-with-r-and-studio-2nd-edition.pdf
Reproducible Research with R and RStudio Second Edition
Reproducibility and Replicability in Science
https://www.nap.edu/catalog/25303/reproducibility-and-replicability-in-science
Books online related to Reproducible Research