This document provides an overview of data management best practices. It discusses the importance of using consistent naming conventions for files and directories to keep data organized. Metadata, controlled vocabularies, and ontologies are presented as essential tools for documenting and allowing others to understand data in the absence of the original researcher. Standards are highlighted as critical for sharing data across disciplines and over time. A variety of tools and repositories are introduced to help with tasks like version control, formatting data for sharing, and archiving datasets for long-term access and attribution. The document emphasizes that properly managing data from the start helps accelerate discovery and ensures reproducibility of scientific research.
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).
Using Open Science to advance science - advancing open data Robert Oostenveld
This document discusses using open science practices like open data to advance science. It notes the benefits of open data like improved reproducibility and opportunities for data mining. However, sharing neuroimaging and other human subject data presents challenges regarding data size, sensitivity, and privacy regulations. The document promotes using the Brain Imaging Data Structure (BIDS) format to organize data in an open, standardized way. It also discusses the gradient between personal/identifiable data that requires protection and de-identified research data that can be shared, as well as legal constraints and appropriate repositories for sharing data responsibly.
The Brain Imaging Data Structure and its use for fNIRSRobert Oostenveld
These slides were prepared for the NIRS toolkit course at the Donders, which due to the Corona crisis has been postponed. The slides present BIDS, explain how fNIRS often involves multiple signals, and relates the two to synchronization and data management
This document summarizes a webinar about managing and preserving scientific data sets. It discusses the definition of science data according to the federal government, why science data is different than other data, current trends and challenges in digital preservation for science. It outlines several levels of digital preservation and provides examples of data being preserved. The webinar discusses the benefits of data management, such as supporting open access and future funding. It also describes existing problems around data management including lack of standards, resources and staffing. Potential solutions discussed include implementing research data management plans and using existing and upcoming tools to help with various stages of the research lifecycle from data creation to long-term preservation and access.
Donders Repository - removing barriers for management and sharing of research...Robert Oostenveld
This is the presentation I gave at the monthly meeting of the Donders Institute PhD council. It shortly explains the Donders Repository, but mainly addresses how to deal with direct and indirectly identifying personal data, with anonymization, pseudomimization and de-identification, and with blurring of research data prior to sharing.
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).
Using Open Science to advance science - advancing open data Robert Oostenveld
This document discusses using open science practices like open data to advance science. It notes the benefits of open data like improved reproducibility and opportunities for data mining. However, sharing neuroimaging and other human subject data presents challenges regarding data size, sensitivity, and privacy regulations. The document promotes using the Brain Imaging Data Structure (BIDS) format to organize data in an open, standardized way. It also discusses the gradient between personal/identifiable data that requires protection and de-identified research data that can be shared, as well as legal constraints and appropriate repositories for sharing data responsibly.
The Brain Imaging Data Structure and its use for fNIRSRobert Oostenveld
These slides were prepared for the NIRS toolkit course at the Donders, which due to the Corona crisis has been postponed. The slides present BIDS, explain how fNIRS often involves multiple signals, and relates the two to synchronization and data management
This document summarizes a webinar about managing and preserving scientific data sets. It discusses the definition of science data according to the federal government, why science data is different than other data, current trends and challenges in digital preservation for science. It outlines several levels of digital preservation and provides examples of data being preserved. The webinar discusses the benefits of data management, such as supporting open access and future funding. It also describes existing problems around data management including lack of standards, resources and staffing. Potential solutions discussed include implementing research data management plans and using existing and upcoming tools to help with various stages of the research lifecycle from data creation to long-term preservation and access.
Donders Repository - removing barriers for management and sharing of research...Robert Oostenveld
This is the presentation I gave at the monthly meeting of the Donders Institute PhD council. It shortly explains the Donders Repository, but mainly addresses how to deal with direct and indirectly identifying personal data, with anonymization, pseudomimization and de-identification, and with blurring of research data prior to sharing.
These are the slides presented by Denis Engemann in the Open Science Panel discussion at the BIOMAG 2018 meeting in Philadelphia. You can find the original version on https://speakerdeck.com/dengemann/mne-hcp-pitch-biomag-2018
CuttingEEG - Open Science, Open Data and BIDS for EEGRobert Oostenveld
Starting with education, inception of research questions, planning, acquisition, analysis and reporting, there are multiple points where Open Science should play a role. In my presentation at the CuttingEEG conference in Paris, I argue that we should not only be sharing primary outcomes as Open Access publications, but that openness involves the full research cycle. Specifically, I will be sharing my experience with Open Data, privacy challenges and possibilities under the GDPR, Open Source for sharing analysis methods, dealing with imperfections in science and versioning of data, code and results. Finally, I will introduce BIDS for EEG, a new effort to increase the impact of shared and well-documented EEG data.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
No Free Lunch: Metadata in the life sciencesChris Dwan
This presentation covers some challenges and makes suggestions to support the work of creating flexible, interoperable data systems for the life sciences.
Research Data Management: What is it and why is the Library & Archives Servic...GarethKnight
This document summarizes research data management and the library and archives service's involvement. It defines research data, explains why data needs to be managed, and outlines the key drivers for data management and publication. It then describes the library and archives service's knowledge of data management, the research data management support service being established, and the guidance, training, and tools being developed to help researchers with data management.
Preservation and institutional repositories for the digital arts and humanitiesDorothea Salo
The document provides advice for humanists on preserving digital scholarship and making preservation someone else's problem. It discusses various options for institutional repositories and digital libraries for housing digital materials. Institutional repositories are described as basic platforms for depositing individual files but have limitations for complex or interactive digital objects. The document recommends exploring what infrastructure an institution already has and getting involved in discussions to implement solutions tailored for the humanities. It also discusses external options like data repositories but notes they often lack support for humanities materials.
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
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.
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).
This document discusses risk management and auditing for digital preservation. It addresses establishing a threat model by understanding what is being preserved and for what purpose. Common threats to digital data include physical medium failure, file format obsolescence, and organizational commitment issues. Audit frameworks like TRAC, DRAMBORA, and SPOT can be used to evaluate repositories, while tools like checksums, migration, and emulation can help mitigate specific risks like bitrot and obsolete formats. Determining file formats and testing file integrity is important for digital preservation.
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management
This document provides guidance on creating a data management plan (DMP). It explains that DMPs are required by many funders to help researchers better organize, document, and preserve their data. The key parts of a DMP include describing the data, metadata standards, data security, archiving and preservation, and access. The presenter provides tips for addressing each part, such as using open formats and partnering with repositories. Resources for creating a DMP at the University of Wisconsin-Milwaukee are also listed.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
Practical Data Management - ACRL DCIG WebinarKristin Briney
This document summarizes a webinar on practical data management. It discusses best practices for file organization, naming conventions, documentation, storage, backups, and ensuring future usability. Key recommendations include organizing files logically by project or type, using consistent naming conventions, thoroughly documenting data collection and analysis methods, storing data in multiple locations both on and off-site, backing up data regularly including testing backups, and future-proofing data through file format conversion and migration to new media. Resources for further information on data management best practices are also provided.
- The document summarizes a workshop on research data management given by Stephanie Simms from the California Digital Library.
- It discusses an overview of research data management and the "SupportYour Data" program, which aims to help researchers better organize, save, document, and share the outputs of their work.
- The workshop covered assessing current data management practices, accessing tools and resources, and data-related services available at Kyoto University.
This document discusses fundamentals of data visualization and how it relates to libraries. It covers topics like vision and the brain, color, tools, and storytelling. It also examines the research cycle and compares what people think happens, what really happens, and what really really happens. Finally, it looks at perceptions of libraries and what activities actually occur in libraries, highlighting the roles of library staff and resources like books, journals, and databases. The document aims to bring data visualization to libraries and have a final conversation about thoughts, comments, and plans related to the topic.
These are the slides presented by Denis Engemann in the Open Science Panel discussion at the BIOMAG 2018 meeting in Philadelphia. You can find the original version on https://speakerdeck.com/dengemann/mne-hcp-pitch-biomag-2018
CuttingEEG - Open Science, Open Data and BIDS for EEGRobert Oostenveld
Starting with education, inception of research questions, planning, acquisition, analysis and reporting, there are multiple points where Open Science should play a role. In my presentation at the CuttingEEG conference in Paris, I argue that we should not only be sharing primary outcomes as Open Access publications, but that openness involves the full research cycle. Specifically, I will be sharing my experience with Open Data, privacy challenges and possibilities under the GDPR, Open Source for sharing analysis methods, dealing with imperfections in science and versioning of data, code and results. Finally, I will introduce BIDS for EEG, a new effort to increase the impact of shared and well-documented EEG data.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
No Free Lunch: Metadata in the life sciencesChris Dwan
This presentation covers some challenges and makes suggestions to support the work of creating flexible, interoperable data systems for the life sciences.
Research Data Management: What is it and why is the Library & Archives Servic...GarethKnight
This document summarizes research data management and the library and archives service's involvement. It defines research data, explains why data needs to be managed, and outlines the key drivers for data management and publication. It then describes the library and archives service's knowledge of data management, the research data management support service being established, and the guidance, training, and tools being developed to help researchers with data management.
Preservation and institutional repositories for the digital arts and humanitiesDorothea Salo
The document provides advice for humanists on preserving digital scholarship and making preservation someone else's problem. It discusses various options for institutional repositories and digital libraries for housing digital materials. Institutional repositories are described as basic platforms for depositing individual files but have limitations for complex or interactive digital objects. The document recommends exploring what infrastructure an institution already has and getting involved in discussions to implement solutions tailored for the humanities. It also discusses external options like data repositories but notes they often lack support for humanities materials.
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
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.
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).
This document discusses risk management and auditing for digital preservation. It addresses establishing a threat model by understanding what is being preserved and for what purpose. Common threats to digital data include physical medium failure, file format obsolescence, and organizational commitment issues. Audit frameworks like TRAC, DRAMBORA, and SPOT can be used to evaluate repositories, while tools like checksums, migration, and emulation can help mitigate specific risks like bitrot and obsolete formats. Determining file formats and testing file integrity is important for digital preservation.
This document provides an overview of key concepts for effective data management, including why data management is important, common data types and stages, best practices for storage, versioning, naming conventions, metadata, standards, sharing, and archiving. It emphasizes that properly managing data helps ensure reproducibility, enables data sharing and reuse, satisfies funder requirements, and supports student work. The presentation covers terminology like metadata ("data about data") and standards like ISO and EML and provides examples to illustrate best practices for documentation to help others understand and use research data. It aims to bring together these concepts to help researchers develop effective Data Management Plans as required by funders like NSF.
This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management
This document provides guidance on creating a data management plan (DMP). It explains that DMPs are required by many funders to help researchers better organize, document, and preserve their data. The key parts of a DMP include describing the data, metadata standards, data security, archiving and preservation, and access. The presenter provides tips for addressing each part, such as using open formats and partnering with repositories. Resources for creating a DMP at the University of Wisconsin-Milwaukee are also listed.
This talk was given by Brianna Marshall, Digital Curation Coordinator, at the UW-Madison Digital Humanities Research Network meeting on December 2, 2014.
Practical Data Management - ACRL DCIG WebinarKristin Briney
This document summarizes a webinar on practical data management. It discusses best practices for file organization, naming conventions, documentation, storage, backups, and ensuring future usability. Key recommendations include organizing files logically by project or type, using consistent naming conventions, thoroughly documenting data collection and analysis methods, storing data in multiple locations both on and off-site, backing up data regularly including testing backups, and future-proofing data through file format conversion and migration to new media. Resources for further information on data management best practices are also provided.
- The document summarizes a workshop on research data management given by Stephanie Simms from the California Digital Library.
- It discusses an overview of research data management and the "SupportYour Data" program, which aims to help researchers better organize, save, document, and share the outputs of their work.
- The workshop covered assessing current data management practices, accessing tools and resources, and data-related services available at Kyoto University.
This document discusses fundamentals of data visualization and how it relates to libraries. It covers topics like vision and the brain, color, tools, and storytelling. It also examines the research cycle and compares what people think happens, what really happens, and what really really happens. Finally, it looks at perceptions of libraries and what activities actually occur in libraries, highlighting the roles of library staff and resources like books, journals, and databases. The document aims to bring data visualization to libraries and have a final conversation about thoughts, comments, and plans related to the topic.
Aryn Mayor provides concise career summaries and resume writing services. Her experience includes positions in staffing, marketing, and as the owner of her own career consulting business. She shares best practices for resume elements, what to include and avoid, and tips for selling oneself to employers through accomplishment-focused content.
This document provides an overview of a course on data visualization fundamentals. The course covers topics like vision and the brain, color, data visualization tools, and storytelling with data. It emphasizes that effective data visualization reveals patterns, communicates ideas, and presents information in a way that offloads cognition by using the power of visual perception. The goal is to teach students how to make their data visually excellent and tell compelling stories without requiring advanced programming or software skills.
This document discusses open science and open data requirements. It notes that funders like NIH now require data sharing plans for large grants and journals require data to be shared publicly. Future policies like FASTR aim to make federally funded research results freely available. Researchers are encouraged to use repositories like the Allen Institute to share data in discoverable, accessible, intelligible, assessable and usable ways. Institutions like OHSU aim to help researchers manage data sharing requirements and make their data more openly available and meaningful through initiatives like Open Insight. While some researchers may be hesitant to share data, doing so can help work towards the common goals of increasing transparency, reproducibility, and value from research efforts.
This document discusses fundamentals of data visualization and storytelling. It covers topics like vision and the brain, color, toolboxes, storytelling, libraries, knowing your data, audience and question. It provides steps for effective storytelling with data which includes gathering and organizing data, writing a story/annotating, knowing your audience, selecting a visualization, applying encodings, and iterating. Examples are provided for visualizing Nathan's Hot Dog Eating Contest data to tell a story about how the International Federation of Competitive Eating may have impacted the competition over time.
This document provides an overview of color fundamentals and data visualization. It begins with an introduction to color and the human visual system. Sections discuss the physics and biology of color, including how light is processed by the eye and brain. Principles of color perception and the opponent-process theory are explained. The document also covers best practices for using color in data visualization, including guidelines for nominal, ordinal, and quantitative scales. Common color schemes and resources for accessible and effective color use are listed.
This document discusses the challenges and opportunities presented by the increasing volume and complexity of biological data. It outlines four main areas: 1) Developing methods to efficiently store, access, and analyze large datasets; 2) Broadening our understanding of gene function beyond a small number of well-studied genes; 3) Accelerating research through improved sharing of data, results, and methods; and 4) Leveraging exploratory analysis of integrated datasets to generate new insights. The author advocates for lossy data compression, streaming analysis, preprint sharing, improved metadata collection, and incentivizing open data practices.
data management, information management, data, big data, personal organization, organization, file management, scientific research, research, project management, data security, file naming conventions, data management plan,
This document provides an overview of data management best practices. It discusses defining data through metadata and naming conventions, dealing with data through version control, backups, and standards, and sharing data through repositories and publications. The presenters recommend describing data thoroughly using metadata, naming files consistently, tracking versions of files, backing up data in multiple locations, using standards like controlled vocabularies, and sharing data to advance science. The OHSU Library can help with all aspects of data management.
Responsible conduct of research: Data ManagementC. Tobin Magle
A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.
This document discusses ways to incentivize scientists to share their data through self-interest. It describes two existing models where data sharing is successful: oceanographic research consortia that require data sharing, and biomedical research projects that organize data generation and sharing through a common platform. The document proposes a distributed graph database and computing platform that would allow researchers to query diverse public and private datasets, providing immediate returns for data sharing. By making others' data useful to analyze and mine, researchers would be competitively disadvantaged not to share their own data. The goal is to enable open sharing by addressing current problems and remaining agile for future needs.
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu (The University of Edinburgh)
- Creating and Managing Digital Research Data in Creative Arts: An overview (2016)
CC BY-NC-SA 4.0
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
This document provides an introduction to data science, including the main roles in data science, the data pipeline process (OSEMN), and steps within the process. It discusses obtaining data from various sources, cleaning data by examining for errors and missing values, exploring data through visualizations and statistics to find patterns, modeling data to create predictive algorithms, and interpreting results through data storytelling. The roles covered include data scientists, data analysts, data engineers, and business intelligence specialists.
How to build a data science project in a corporate setting, by Soraya Christi...WiMLDSMontreal
"Applied data science in the industry: How to build a data science project in a corporate setting - best practices and a real-world example"
By Soraya Christina, Senior Data Scientist at Morgan Stanley
Abstract:
- Which platforms/technology to use for your analytics project and why? (Spark, Hadoop, vendor products, open source, Python, Scala, etc?)
- How to build your data science flow and what to avoid? (Occam's razor, testable and structured flow)
- How to presents results in a way business stakeholders understand them? (Making complex concepts easy to understand by business lines)
- A real-world example of a real-time failure prediction using Spark streaming and ML components.
The purpose of this talk is to present the challenges and solutions when building data science projects in a corporate environment. Generating insights for better business decision making is what drives data science projects. But working with business side by side, being able to build a reliable flow and properly communicating results and key elements are more than crucial, it is what will guarantee the success of your data science projects.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
The document discusses the history and evolution of data warehousing. It notes that data warehousing emerged due to technological limitations that prevented transactional and analytical uses of data on the same platform. Early stages included departments storing unused data to avoid tape changes and government projects consolidating databases. Factors like business reengineering and a focus on continuous improvement drove more analytical uses of data. Key lessons discussed include the importance of business culture supportive of fact-based decision making and managing political issues raised by data warehouses. The document advocates for keeping metadata simple and focused on understandability and findability of data.
Data discovery and metadata - Natasha Simons
Research Data Management workshop at the iSchools Data Science Winter Institute, 7-9 December 2017, University of Hong Kong
Karen Lopez 10 Physical Data Modeling BlundersKaren Lopez
Karen Lopez's presentation about 10 Physical Data Modeling/Database Design blunders, based on her work in helping organizations get the most value out of their models and data.
Notice an error? Let me know. I welcome this sort of feedback.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
This workshop was presented in Riyadh, SA in 21-22 Jan 2019, with the collaboration with Riyadh Data Geeks group.
To learn more about the workshop please see this website:
http://bit.ly/2Ucjmm5
- Data challenges are growing in terms of volume, variety, velocity and quality. There is no single solution and real-world solutions will be hybrid.
- Metadata management is a huge challenge, even basic metadata is beyond most small organizations. Federated systems are needed to transform medicine.
- The document discusses challenges with data management across various domains including life sciences, healthcare, genomics, machine learning, artificial intelligence, and personal data. It emphasizes the importance of data visibility, quality, and integration across siloed systems.
This document discusses securing sensitive data in databases. It begins by defining different types of sensitive data, including government IDs, medical data, financial data, and intellectual property. Next, it discusses why protecting sensitive data is important, such as guarding against identity theft and fraud, ensuring privacy, and complying with regulations. The presentation then covers techniques for securing data, including data classification, encryption, hashing, masking, coding, and limiting data storage. It demonstrates how to use cell-level encryption and transparent data encryption in SQL Server. Overall, the document provides an overview of best practices for classifying, handling, and technically protecting sensitive data in databases.
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document provides an introduction to the fundamentals of data visualization. It begins with an overview of vision and the brain, covering topics like visual biology, visual processing, and how the brain interprets different visual cues. The document then outlines several subsequent chapters that will discuss principles of color, toolkits for visualization, storytelling with data, and libraries of visualization types. It includes various images and cites sources to support the overview of visual concepts.
This document provides an introduction to fundamentals of data visualization including pre-attentive processing, Gestalt principles, statistical definitions, and different types of visual encodings and graphing techniques. It discusses using visual cues like color, pattern, and position to encode data values and highlights the importance of avoiding inappropriate triggering of pre-attentive processes to create effective visualizations. Examples of both good and bad real-world data visualizations are also provided.
This document provides tips for effective science writing. It discusses four key constraints: audience, format, mechanics, and politics. Some practical tips include using verbs to describe actions, keeping subjects near verbs for clarity, and placing new information at the end of sentences. Cohesion, coherence and emphasis are important, such as introducing familiar information before new details. The document stresses keeping writing concise and clear. Effective science communication is important, and the key is to practice writing skills.
This document provides guidance on creating effective posters for presenting research. It discusses including a brief title, introduction, materials and methods, results, and conclusions sections. Visual elements should be used extensively, with minimal text. Images and graphics should have labels and scales. Consistent formatting and high contrast between text and background is advised. Effective use of color and layout is also covered. The document emphasizes practicing the poster presentation to feel comfortable speaking about the research. Contact information should be clearly displayed.
This document provides an overview of fundamentals for creating effective data visualizations. It discusses:
1) Why visualization is important for communicating information and insights from data.
2) Key aspects of describing data meaningfully, including distinguishing between quantitative and qualitative data types.
3) Best practices for visual encoding techniques that maximize pre-attentive processing and clarity of visuals.
4) The steps involved in making a descriptive, unique and engaging data graphic, including gathering and organizing data, writing annotations to tell a story, selecting appropriate visualizations, and experimenting through iteration.
Presented at the Oregon Special Libraries Association workshop: Customizing Data Delivery to Target Audiences.
Join ORSLA Members and friends for a half-day workshop on Customizing Data Delivery to Target Audiences.
When: Friday, September 27, 1pm – 4pm with a no-host happy hour to follow.
Architectural Heritage Center (701 SE Grand Avenue, Portland, http://www.visitahc.org/)
You’ve done your research and have a pile of information. Learn how to turn that information into an impressive research package by profiling your audience (clients, faculty, students, management), assessing their needs and learning styles, and creating meaningful charts and info-graphics guaranteed to impress.
Schedule:
1-2pm – Reece Dano: Ethnographic Research: Going Beyond WHAT and Getting to WHY
2-3pm – Temese Szalai: Delivering Insights, Not Information
3-4pm – Jackie Wirz: Fundamentals of Data Visualization: Creating Beautiful, Elegant and Descriptive Visual Displays
After 4pm – Happy hour at [to be announced]
The document discusses the importance of good data management practices for reasons such as ensuring reproducibility of research, accelerating scientific discovery, and giving proper credit to data sources. It provides an overview of best practices for data management, including using informative file naming and organization, creating detailed metadata, employing controlled vocabularies and ontologies, utilizing database and file management tools, and sharing data through appropriate repositories. The presenters are experts on data management who aim to help researchers overcome common frustrations and adopt better strategies.
The document discusses research and the research cycle. It describes both the idealized view of the research cycle as a linear process from idea to fame, as well as the messier reality. It also mentions sources for new ideas such as existing data, literature, conferences, colleagues and team science. Finally, it lists many research centers and resources available at OHSU to support the research process.
Powered by Libraries: Leveraging Libraries for Semantic Web and Linked Open D...Jackie Wirz, PhD
While data is the cornerstone of scientific research, traditional mechanisms of research assessment overlook these data outputs, instead focusing solely on publications. However, publications are just the tip of the iceberg: in reality, science is based on a complex landscape of research data and activities, which can be published or shared beyond traditional journal articles. Information scientists and software engineers are now working to relate and make accessible all of this data for research networking, research evaluation, resource sharing, and hypothesis discovery. Furthermore, federal funding agencies are increasing their requirements for data sharing and data standardization. Researchers, though, are often largely unaware of data standardization efforts and tools to access shared data.
In order to deal with this onslaught of data, standards, and tools, universities are asking libraries to play an increasing role in information management strategies. This includes training, data housing, and dissemination of information about tool resources. Libraries are at a key intersection between the research community and semantic engineers, and are increasingly hiring specialists with a research background to provide data modeling, curation, and scientific information dissemination services. As a result, libraries have been working closely with the research community to build and integrate semantic tools into the entire research cycle. Librarians can help researchers understand ways to interpret and share their data, and use tools to query the large amounts of existing data.
NCBI has developed a powerful suite of online biomedical and bioinformatics resources, including old friends like PubMed and OMIM and newer resources such as Genome. This collection of databases and tools are widely used by scientists and medical professionals across the world. With such a wealth of information, it is easy to get overwhelmed. Join us for an overview to NCBI resources for the information professional with an emphasis on biodata connectivity. No science degree required!
This document provides an overview of science 101 and key concepts in cell biology and biochemistry. It begins with defining science and the different disciplines within biomedical health sciences. It then covers cells and their components, the central dogma of DNA transcription and RNA translation, and the molecular architecture of DNA, RNA and proteins. Specific topics discussed include eukaryotic and prokaryotic cells, organelles, the nucleus and DNA packaging, RNA structures and functions, amino acids, protein folding and secondary structures.
This preview is for a continuing education event rated as general audiences for all ages that will be held at the 2011 OHSLA meeting. Drs. Wirz and Nelsen will present the session "It Came from the Lab! Science 101" which will give a quick review of basic science concepts in an entertaining and nerdy way to help attendees who may be scared of science or don't know where to begin learning. The session aims to make continuing education fun and help attendees feel more comfortable with science.
The NCBI Boot Camp for Beginners was designed to offer an overview of the NCBI suite of resources. In the first half of the presentation, highlighted databases were covered in four main categories: literature, sequences, genes & genomes and expression & structure. The second half of the class used the apolipoprotein A as a query that was explored through many of the NCBI databases, from identifying the reference sequences to a structural analysis of the Cys130Arg variant.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
27. 1. Brilliant Idea!
2. Design Experiment
3. Do Experiment
The data timeline
4. Collect data
5. Compile and Analyze
6. Publish
7. Fame, Fortune
28. 1. Brilliant Idea!
2. Design Experiment
3. Do Experiment
4. Collect data
The data timeline:
What people think
5. Compile and Analyze
6. Publish
7. Fame, Fortune
29. Idea!
Analyzing data
The data timeline:
What Happens
experiment
Compile data
design
Other
People’s
data
Try #2
Failure!!
beer
#896
#896
6. Publish
36. a. Storing data
b. Backing up data
c. Analyzing/manipulating data
d. Finding data produced by other researchers
e. Ensuring data are secure
f. Making data accessible to other researchers
g. Controlling access to data
h. Tracking updates to data (ie versioning)
i. Creating metadata
j. Protecting intellectual property rights
k. Ensuring appropriate professional credit/citation is given
37. How do I not go crazy?
naming|metadata |standards | tools
67. a. Save copies of data on a disk, USB drive, or computer
hard drive
b. Save copies of data on a local server
c. Save copies of data on a central campus server
d. Save copies of data on a web based or cloud server
e. Store data in a repository or archives
f. Automatically backup files
g. Manually generate backup
h. Restrict access to files
68. 3 | copies (you, lab, other)
2 | 2 different forms
1 | remote location
123. Digital Object Identifier (DOI)
Example: 10.1371/journal.pbio.1001339
Unique resource identifier (URI)
A URI will resolve to a single location on the web
URIs for people
Repositories use Unique IDs
127. Thinking Beyond the PDF
Raw Science
Small publications
Self-publishing
Datasets
Nanopublications
Blogging
Code
Argument or
passage
Social Media
Experimental
design
Single figure
publications
Comments &
Reviews
Annotations