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.
The document provides guidance on writing a data management plan (DMP). It explains that DMPs are now required by many funders to accompany grant applications. A DMP outlines how research data will be managed and shared during and after a project. It should address issues like the type of data being collected, documentation, storage and backup plans, data sharing and reuse, legal and ethical concerns, and long-term preservation. Writing a DMP helps ensure good data management practices and that a project is compliant with funder policies supporting open access to research data.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
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.
The document discusses advances in data management practices and technologies for ecosystem science. It describes the role of a data manager in facilitating data management, from collecting raw data to organizing it in standard formats and metadata according to community practices. Well-managed data is stored and shared through repositories to enable discovery, access, interoperability and future reuse. Resources and experts are available to help researchers improve their data management.
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.
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.
The document provides guidance on writing a data management plan (DMP). It explains that DMPs are now required by many funders to accompany grant applications. A DMP outlines how research data will be managed and shared during and after a project. It should address issues like the type of data being collected, documentation, storage and backup plans, data sharing and reuse, legal and ethical concerns, and long-term preservation. Writing a DMP helps ensure good data management practices and that a project is compliant with funder policies supporting open access to research data.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
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.
The document discusses advances in data management practices and technologies for ecosystem science. It describes the role of a data manager in facilitating data management, from collecting raw data to organizing it in standard formats and metadata according to community practices. Well-managed data is stored and shared through repositories to enable discovery, access, interoperability and future reuse. Resources and experts are available to help researchers improve their data management.
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 template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
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
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.
This document provides guidance on research data management and developing data management plans. It discusses why managing research data is important, including making research easier to conduct, avoiding accusations of fraud or bad science, and getting credit for data produced. The document outlines what is involved in research data management and considerations for sharing and preserving data, such as file formats, documentation, and standards. It emphasizes the importance of data management planning and provides tips on developing plans to meet funder requirements.
Lesson 2 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.
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
This document discusses data curation, which involves maintaining and preserving digital research data throughout its lifecycle. It defines scientific data as factual material necessary to validate research findings, including observational, experimental, simulation, and derived data. The data lifecycle is described using the DataONE model. Key points covered include funder requirements for data management plans from NSF and NIH, benefits of data curation such as transparency and allowing others to analyze and build upon the data, best practices for file naming, types and storage, use of metadata, and resources for data sharing and curation.
Research Data Management Introduction: EUDAT/Open AIRE Webinar| www.eudat.eu | EUDAT
This webinar discusses research data management. It explains why managing data is important for reproducibility, avoiding data loss, and meeting funder requirements. It outlines Horizon 2020's requirements for open data and describes services from EUDAT and OpenAIRE that can help with the entire data lifecycle from creation to long-term preservation and sharing. The webinar covers best practices like creating data management plans, metadata, using standards, licensing, and selecting repositories to archive and share research data.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
This document provides an overview of FAIR data principles and the FAIR data ecosystem. It discusses what FAIR data is, including that FAIR data aims to support communities in publishing and utilizing scientific data and knowledge in a findable, accessible, interoperable, and reusable manner. It then describes the different levels of the FAIR data ecosystem, including normative principles, standards in the FAIR data protocol, FAIR data resources that comply with these standards, and systems/tools that use FAIR data. It provides examples of converting raw data into FAIR data resources and the potential applications of a FAIR data ecosystem.
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.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
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.
Essentials 4 Data Support: a fine course in FAIR Data SupportEllen Verbakel
The document summarizes the Essentials 4 Data Support (E4DS) course, which teaches people how to support researchers in storing, managing, archiving, and sharing research data according to FAIR principles. The course covers topics like data documentation, identifiers, formats, metadata, and licensing. It is offered online or in a blended format over 6 weeks. The goal is to educate data supporters so that researchers can find, access, interoperate with, and reuse each other's data in a fair manner.
This document provides an overview of developing a data management plan. It discusses the Digital Curation Centre and the speaker's involvement with DMPs. A DMP is a plan for managing research data throughout the data lifecycle that addresses issues like data capture, documentation, access, storage, backup, and long-term preservation. Developing a DMP ensures good data practices and maximizes data reuse. It also benefits research by making the process more efficient, data more accessible and transparent, and findings more impactful. A DMP typically involves researchers, institutions, partners and other stakeholders. Funders like the European Union also have specific DMP requirements for projects seeking funding.
C606 the pan american health organizations health information and intelligenc...Ramon Martinez
This poster presents the design and implementation of PAHO’s Health Information and Intelligence Platform (PHIP), an organization-wide resource that provides public health data, analytical methods and tools, and information to support decision-making in public health within PAHO. PHIP also provides information products and evidence to national health authorities from Member States of the Americas, health professionals and the general public
T4N - Session 2: Introduction to data curation and analysis processesTransformNutritionWe
Data curation involves aggregating, structuring, and reporting nutrition data from various sources at country and global levels. This includes collecting primary data, organizing it using common variables and metadata, and reporting summaries. Data analysis synthesizes this curated data to build tools and models that derive insights. It combines information to analyze trends, identify at-risk groups, and help answer questions. Challenges include restricted data access, lack of statistical capacity, and underutilized data visualization. Recommendations are to invest in data management systems, build analytical capacity, make codes and tools openly available, and automate standardized reporting.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
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
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.
This document provides guidance on research data management and developing data management plans. It discusses why managing research data is important, including making research easier to conduct, avoiding accusations of fraud or bad science, and getting credit for data produced. The document outlines what is involved in research data management and considerations for sharing and preserving data, such as file formats, documentation, and standards. It emphasizes the importance of data management planning and provides tips on developing plans to meet funder requirements.
Lesson 2 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.
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
An introduction to the FAIR principles and a discussion of key issues that must be addressed to ensure data is findable, accessible, interoperable and re-usable. The session explored the role of the CDISC and DDI standards for addressing these issues.
Presented by Gareth Knight at the ADMIT Network conference, organised by the Association for Data Management in the Tropics, in Antwerp, Belgium on December 1st 2015.
This document discusses data curation, which involves maintaining and preserving digital research data throughout its lifecycle. It defines scientific data as factual material necessary to validate research findings, including observational, experimental, simulation, and derived data. The data lifecycle is described using the DataONE model. Key points covered include funder requirements for data management plans from NSF and NIH, benefits of data curation such as transparency and allowing others to analyze and build upon the data, best practices for file naming, types and storage, use of metadata, and resources for data sharing and curation.
Research Data Management Introduction: EUDAT/Open AIRE Webinar| www.eudat.eu | EUDAT
This webinar discusses research data management. It explains why managing data is important for reproducibility, avoiding data loss, and meeting funder requirements. It outlines Horizon 2020's requirements for open data and describes services from EUDAT and OpenAIRE that can help with the entire data lifecycle from creation to long-term preservation and sharing. The webinar covers best practices like creating data management plans, metadata, using standards, licensing, and selecting repositories to archive and share research data.
Introduction to the Environmental Data Initiative (EDI)Corinna Gries
The Environmental Data Initiative enables the environmental science community to maximize knowledge development through the reusability of FAIR environmental data by providing curation services, training, and a robust and modern data repository.
Please cite as: Gries, Corinna. (2018, December). Introduction to the Environmental Data Initiative (EDI) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.4672376
The document summarizes a pilot project at the University of Edinburgh to support the development of a UK Research Data Discovery Service. PhD interns engaged with researchers from various schools to describe and deposit research datasets in the university's systems to be harvested by the discovery service. Observations found mixed results across schools, with humanities researchers less comfortable sharing data due to copyright and reluctance to share interpretations. Other schools had established data repositories causing less interest in the university's system. Building research data management practices will require tailored approaches and more training over time.
This document provides an overview of FAIR data principles and the FAIR data ecosystem. It discusses what FAIR data is, including that FAIR data aims to support communities in publishing and utilizing scientific data and knowledge in a findable, accessible, interoperable, and reusable manner. It then describes the different levels of the FAIR data ecosystem, including normative principles, standards in the FAIR data protocol, FAIR data resources that comply with these standards, and systems/tools that use FAIR data. It provides examples of converting raw data into FAIR data resources and the potential applications of a FAIR data ecosystem.
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.
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
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.
Essentials 4 Data Support: a fine course in FAIR Data SupportEllen Verbakel
The document summarizes the Essentials 4 Data Support (E4DS) course, which teaches people how to support researchers in storing, managing, archiving, and sharing research data according to FAIR principles. The course covers topics like data documentation, identifiers, formats, metadata, and licensing. It is offered online or in a blended format over 6 weeks. The goal is to educate data supporters so that researchers can find, access, interoperate with, and reuse each other's data in a fair manner.
This document provides an overview of developing a data management plan. It discusses the Digital Curation Centre and the speaker's involvement with DMPs. A DMP is a plan for managing research data throughout the data lifecycle that addresses issues like data capture, documentation, access, storage, backup, and long-term preservation. Developing a DMP ensures good data practices and maximizes data reuse. It also benefits research by making the process more efficient, data more accessible and transparent, and findings more impactful. A DMP typically involves researchers, institutions, partners and other stakeholders. Funders like the European Union also have specific DMP requirements for projects seeking funding.
C606 the pan american health organizations health information and intelligenc...Ramon Martinez
This poster presents the design and implementation of PAHO’s Health Information and Intelligence Platform (PHIP), an organization-wide resource that provides public health data, analytical methods and tools, and information to support decision-making in public health within PAHO. PHIP also provides information products and evidence to national health authorities from Member States of the Americas, health professionals and the general public
T4N - Session 2: Introduction to data curation and analysis processesTransformNutritionWe
Data curation involves aggregating, structuring, and reporting nutrition data from various sources at country and global levels. This includes collecting primary data, organizing it using common variables and metadata, and reporting summaries. Data analysis synthesizes this curated data to build tools and models that derive insights. It combines information to analyze trends, identify at-risk groups, and help answer questions. Challenges include restricted data access, lack of statistical capacity, and underutilized data visualization. Recommendations are to invest in data management systems, build analytical capacity, make codes and tools openly available, and automate standardized reporting.
Collecting Health Data in Africa - Peter Hessels - KITopenforchange
This document discusses collecting health data in Africa and lessons that can be learned. It covers existing health datasets like the Demographic Health Survey and District Health Information System. The Health Metrics Network works with 83 countries to strengthen their national health information systems. Lessons include the importance of reliable data, addressing privacy and consent issues when collecting data, ensuring data quality and can be analyzed and visualized, and the value of standardizing data and indicators through collaboration.
DARTNet is a non-profit research institute that facilitates collaboration between practice-based research networks (PBRNs) and academic partners. It aggregates electronic health record, claims, and patient-reported data to power quality improvement efforts and comparative effectiveness research. DARTNet provides data extraction and transformation software, clinical decision support, and performance reporting to help member organizations with meaningful use requirements and quality improvement. Its goal is to improve health and healthcare through collaborative, data-driven research and learning.
The document discusses the NIH's efforts to create a modernized, integrated, and FAIR biomedical data ecosystem. It outlines the NIH Office of Data Science Strategy's goals of optimizing data infrastructure and management, developing tools and the workforce, and ensuring stewardship and sustainability. It describes specific initiatives like STRIDES, the Generalist Repository Ecosystem Initiative, and AIM-AHEAD which aim to improve data sharing, train researchers, and address health disparities through AI. The overall goal is to make biomedical data more accessible, interoperable, and useful to advance biomedical research.
MakeHx is a health history platform that allows individuals to aggregate their health data from various devices and share it with healthcare providers. It aims to give both individuals and providers a comprehensive view of an individual's health history to facilitate diagnoses and treatment. The company is seeking $175,000 in funding to continue development beyond its September launch and acquire more users. It offers a free service to individuals while providers pay $50 per month for additional tools.
This document provides an overview and agenda for Week 8 of the Data Scientist Enablement (DSE) 400 program. It outlines the week's discussions on ethics in big data, recommended learning materials, activities including exploring datasets and starting a blog, and an assignment to cleanse and visualize a sensor dataset or complete an alternative task. The timeline for the full DSE program and options for adaptive learning and proficiency certification are also summarized.
Project Designing a Database using MS AccessDescription Work.docxwoodruffeloisa
Project: Designing a Database using MS Access
Description: Working in a team of two members, you have to design a Database for an organization.
The project will be completed and submitted incrementally. You will have to submit the project in three phases. Every increment submission will be accompanied by a presentation of completed work.
In the first phase you need to work on the following: (30 points)
1. Determine the purpose of your database
2. Design the DFD (Data Flow Diagram) for your system. Recommended tool to design DFD is MS Visio or Visual Paradigm but it is not required.
3. Design the ERD (Entity Relationship Diagram)
In the second phase you need to work on the following: (40 points)
1. Find and organize the information required
2. Divide your information items into major entities or subjects, such as Products or Orders. Each subject then becomes a table.
3. Decide what information you want to store in each table. Each item becomes a field, and is displayed as a column in the table. For example, an Employees table might include fields such as Last Name and Hire Date.
4. Choose each table’s primary key. The primary key is a column that is used to uniquely identify each row. An example might be Product ID or Order ID.
In the third & final phase you need to the following: (30 points)
1. Apply the data normalization rules to see if your tables are structured correctly. Make adjustments to the tables, as needed.
2. Write SQL (Structured Query language) to retrieve the data
Briefly described your healthcare organization, including its culture and readiness for change.
SLIDE 3
Described the current problem or opportunity for change. The circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general was described.
Proposed an evidence-based idea for a change in practice using an evidence-based practice approach to decision making.
SLIDE 5
• Described your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
SLIDE 6
Described the measurable outcomes.
RECOMMENDING AN EVIDENCE-BASED PRACTICE CHANGE
student’s name
instructor
course
date
Organizational Culture And Readiness For Change
The first assessment will help to determine the ability of the current organization to accept change to the nursing practice
Prior assessment of the organization’s challenges to change embracement is key to provide solutions to health care problems in the society
According to the Organization Culture Assessment Instrument (OCAI), the staff responses indicate the willingness of the organization to change
Current Problem Or Opportunity For Change
The need for change is to reduce falls
The main barrier to this facility is lack of sufficient knowledge about evidence-based practice implementation
Patients with falls require a long duration of hospital stay ...
The document proposes a vision to build a health data infrastructure that shares every public health file from California counties, nonprofits, and state and national organizations. It describes a process to convert datasets to common formats, clean the data, and distribute it to websites and analysts' desktops for easy analysis. This would be done through an industrial data management system called Ambry.io at a cost of $1000 per month per county. The system involves data wranglers converting datasets for $100 each and systems administrators selecting datasets for analysts. This would make health data easier to find, understand, and use to allow more analysis and coordination between organizations.
The Shanghai Health Bureau Information Center has been developing long-term plans for Shanghai's health information system since 1998. It creates rules and standards for building the system to effectively network medical information. The system's information network provides public health services and supports administrative decisions. It also coordinates subordinate bodies to build information resources and provides training and services to the health sector.
How to Use Your Database to Power Your Fundraising - FINZ 2014 PresentationBlackbaud Pacific
This document provides tips on how nonprofit organizations can use their fundraising databases more effectively. It discusses the importance of clean, well-maintained data and designating a database manager. Current fundraising trends like donor retention, source diversification, and new channels are also covered. The document encourages organizations to monitor key metrics in their database like donor acquisition and retention rates to inform strategy. Automating processes and focusing on high-value donors are presented as ways to maximize database potential.
Using HealthyCity.org to upload your own data for planning and policy changeHealthy City
This document provides information on how to use HealthyCity.org, an online mapping platform, to upload community data for planning and policy change. It outlines how to participate in a webinar on the site, describes the types of data that can be uploaded including services, population characteristics, and health metrics. It then discusses collecting primary data through focus groups, interviews, surveys and participatory mapping to map available and unavailable resources in a community. Methods are provided for collecting outdoor exercise equipment data at local parks as an example. Additional data collection resources are also listed.
IHP 670 Logic Model Outline TemplateUse this template to hMalikPinckney86
IHP 670 Logic Model Outline Template
Use this template to help you design the logic model outline for your program plan. Fill in the information identified in each section of the outline’s bracketed text, and then delete the bracketed text before submitting this outline for grading.
Program Title:
Program Goal:
Inputs or Resources
Activities or Work Products
Outcomes
[Insert all inputs or resources for your program here.]
[Insert all activities or work products for your program here.]
Short-term Outcomes (2 Weeks)
[Insert all short-term outcomes for your program here.]
Intermediate Outcomes (6 Weeks)
[Insert all intermediate outcomes for your program here.]
Long-term Outcomes (4 Months)
[Insert all long-term outcomes for your program here.]
IHP 670 Outcome Measure Table Template
Use this template to help you identify the outcome measures for your program plan presentation. Fill in the information identified in the table’s bracketed text, and then delete the bracketed text before adding the completed table to your project submission. If you need additional rows for outcome measures, copy and paste the last row of the table as many times as needed.
Outcome Measure
Success Target
Time to Achieve Success Target
Tool for Documenting Data
[Insert outcome measure here.]
[Insert success target information here.]
[Insert time to achieve success target information here.]
[Insert the tool used for document data here.]
[Insert outcome measure here.]
[Insert success target information here.]
[Insert time to achieve success target information here.]
[Insert the tool used for document data here.]
[Insert outcome measure here.]
[Insert success target information here.]
[Insert time to achieve success target information here.]
[Insert the tool used for document data here.]
[Insert outcome measure here.]
[Insert success target information here.]
[Insert time to achieve success target information here.]
[Insert the tool used for document data here.]
Pediatric Fertility Preservation
Project evaluation
Veronica Horne
Southern new Hampshire university
Ihp 670
April 10, 2022
Introduction
My project is to create a fertility preservation program in Bellevue Hospital for children diagnosed with cancer and similar chronic conditions.
Fertility preservation evaluates children with medical conditions and treatments that could affect fertility.
Evaluating the success of a healthcare program is important because it helps in obtaining a systematic method to study a program to understand how well it achieve its goals.
Evaluating a fertility preservation program helps will help me to determine what works well and what could be improved.
The method I will use to evaluate my project is the use of focus groups.
Evaluation method-Focus Groups
Focus groups is suitable for collecting data for fertility preservation because it is reliable.
It is primarily quantitative but can also be used to collect quantitative data.
When used for quantitativ ...
The Health Catalyst Data Operating System (DOS™): Lessons Learned and Plans ...Health Catalyst
Just over three years ago, Health Catalyst publicly announced the development of the Data Operating System (DOSTM). Conceptually, DOS goes back more than 20 years as a single platform that could support what Dale Sanders calls the “Three Missions of Data”—analytics, data-first application development, and interoperability.
“Data platforms are the next evolution of the technology stack,” Sanders says. While the Cloud made infrastructure an easy and scalable platform, modern operating systems and programming languages made software platforms scalable and easy to build. He cautions, however, “Data wrangling, especially in healthcare, is still a giant challenge.” Sanders explains that DOS is therefore an essential strategy for Health Catalyst, as well as an important new concept in the world of platforms.
“DOS and its concept is a data platform that makes analytics, app development, and interoperability easy and scalable,” Sanders says.
In this webinar, Sanders and Bryan Hinton will review the concept of a data operating system and the vision behind it. Hinton, who leads the DOS team for Health Catalyst, will reflect on lessons learned over the past three years and what he has planned for the future.
The document discusses the importance of data for non-profits and provides tips on finding, analyzing, and using different types of data. It explains how to create simple databases and use data for needs assessments, funding applications, evaluations, and determining community priorities. External sources of data are also identified like government agencies and surveys that can help non-profits with their work.
Running Head: WEEK 1 1
WEEK 1 4
Analysis of Asthma Patients
Course
March 25, 2020
Introduction:
Information processing is any method by which the retrieval or assistance in the retrieval of information is planned. Selection is accomplished by seeking and receiving the necessary data from persons or organizations via the correct vehicle. The data is given explicitly by the respondent (self-enumeration) or by the investigator. Set also involves the retrieval of institutional details. Data collection applies to any mechanism that transforms the information given to the respondent into an electronic format. The process is either automatic or requires the workers to plugging the gathered data (keys). Data coding is any method that assigns a numeric value to an answer. Programming is frequently automatic, but more complicated decisions typically need human input (coders). Survey operations also require a large level of optimization, which contributes to the accessibility of data, knowledge relevant to the survey phase. Instances of para data include an indication of whether or not a device is in the survey, a list of calls and meetings, a record of keystrokes (audit record), a system of compilation, managerial details (e.g. interview blog) and expense details. Data is not just a source of statistics, it is also the primary interaction that a polling organization has with the population who wants to be encouraged to participate. Data collection and encoding are the structured data for use as output by all future survey operations. Data processing, data analysis, and coding activities frequently entail a substantial portion of the research expenditure and require considerable humor. Plan the collection procedure to reduce the stress on the participant and the expense of processing, and to optimize timeliness and quality of the results. Data may be obtained by self-reporting, voice interviews or informal interviews through either a document or an online survey (e.g. automated data recording, the Web, computer-assisted interviewing).
What is PHIS?
Global Health Information Systems (PHIS) are the essential elements of public health services, offering details about how community programs obtain and manage public health treatment data. Such results help public health initiatives, such as the monitoring of illnesses or the implementation of public health systems for teen smokers. Countries also create PHIS via the state health department in order to collect data that will be used relevantly to assess the health condition of the country. In this report, the PHIS toolkit will be used in order to analyze the patients of asthma, and database records have been collected.
Asthma Database Analyses:
Create effective sample management protocols and controls for all data gathering .
Running Head: WEEK 1 1
WEEK 1 4
Analysis of Asthma Patients
Course
March 25, 2020
Introduction:
Information processing is any method by which the retrieval or assistance in the retrieval of information is planned. Selection is accomplished by seeking and receiving the necessary data from persons or organizations via the correct vehicle. The data is given explicitly by the respondent (self-enumeration) or by the investigator. Set also involves the retrieval of institutional details. Data collection applies to any mechanism that transforms the information given to the respondent into an electronic format. The process is either automatic or requires the workers to plugging the gathered data (keys). Data coding is any method that assigns a numeric value to an answer. Programming is frequently automatic, but more complicated decisions typically need human input (coders). Survey operations also require a large level of optimization, which contributes to the accessibility of data, knowledge relevant to the survey phase. Instances of para data include an indication of whether or not a device is in the survey, a list of calls and meetings, a record of keystrokes (audit record), a system of compilation, managerial details (e.g. interview blog) and expense details. Data is not just a source of statistics, it is also the primary interaction that a polling organization has with the population who wants to be encouraged to participate. Data collection and encoding are the structured data for use as output by all future survey operations. Data processing, data analysis, and coding activities frequently entail a substantial portion of the research expenditure and require considerable humor. Plan the collection procedure to reduce the stress on the participant and the expense of processing, and to optimize timeliness and quality of the results. Data may be obtained by self-reporting, voice interviews or informal interviews through either a document or an online survey (e.g. automated data recording, the Web, computer-assisted interviewing).
What is PHIS?
Global Health Information Systems (PHIS) are the essential elements of public health services, offering details about how community programs obtain and manage public health treatment data. Such results help public health initiatives, such as the monitoring of illnesses or the implementation of public health systems for teen smokers. Countries also create PHIS via the state health department in order to collect data that will be used relevantly to assess the health condition of the country. In this report, the PHIS toolkit will be used in order to analyze the patients of asthma, and database records have been collected.
Asthma Database Analyses:
Create effective sample management protocols and controls for all data gathering .
Effective utilisation and allocation of health resources across the Midlands region in New Zealand. Presented by Samuel Mackenzie & Brent Harvey, HealthShare, at HINZ 2014, 12 November 2014, 11.37am, Marlborough Room
This document outlines the requirements for a workforce plan assignment for a health organization or hospital in NSW. Students are asked to prepare a 2000 word workforce plan that includes an executive summary, environmental scans both internal and external to the organization, a workforce profile using available data, identification of critical workforce issues, and recommendations of strategies to address the issues. Key areas to be covered in the strategies include recruitment, performance management, skills development, workforce redesign, education and training, and efficiency. Confidential organizational data may be requested and analyzed to inform the workforce profile and issues.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
These set of slides were presented at the BEP Seminar "Targeting in Development Projects: Approaches, challenges, and lessons learned" held last Oct. 2, 2023 in Cairo, Egypt
Caitlin Welsh
POLICY SEMINAR
Food System Repercussions of the Russia-Ukraine War
2023 Borlaug Dialogue Breakout session
Co-organized by IFPRI and CGIAR
OCT 26, 2023 - 1:10 TO 2:10PM EDT
Joseph Glauber
POLICY SEMINAR
Food System Repercussions of the Russia-Ukraine War
2023 Borlaug Dialogue Breakout session
Co-organized by IFPRI and CGIAR
OCT 26, 2023 - 1:10 TO 2:10PM EDT
Antonina Broyaka
POLICY SEMINAR
Food System Repercussions of the Russia-Ukraine War
2023 Borlaug Dialogue Breakout session
Co-organized by IFPRI and CGIAR
OCT 26, 2023 - 1:10 TO 2:10PM EDT
Bofana, Jose. 2023. Mapping cropland extent over a complex landscape: An assessment of the best approaches across the Zambezi River basin. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
Mananze, Sosdito. 2023. Examples of remote sensing application in agriculture monitoring. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
This document discusses using satellite data and crop modeling to forecast crop yields in Mozambique. It summarizes previous studies conducted in the US, Argentina, and Brazil to test a remote sensing crop growth and simulation model (RS-CGSM) for predicting corn and soybean yields. For Mozambique, additional data is needed on crop cultivars, management practices, planting and harvest seasons. It also describes using earth observation data and machine learning models to forecast crop yields and conditions across many countries as part of the GEOGLAM program, though this is currently only implemented in South Africa for Africa. Finally, it mentions a production efficiency model for estimating yield from satellite estimates of gross primary production.
International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. PowerPoint presentation given during the Project Kickoff Meeting (virtual), January 12, 2023
International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 1. Stakeholder engagement for impacts. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
Centro de Estudos de Políticas e Programas Agroalimentares (CEPPAG). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 3. Digital collection of groundtruthing data. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
ITC/University of Twente. 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 2. Enhanced area sampling frames. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
Christina Justice
IFPRI-AMIS SEMINAR SERIES
A Look at Global Rice Markets: Export Restrictions, El Niño, and Price Controls
Co-organized by IFPRI and Agricultural Market Information System (AMIS)
OCT 18, 2023 - 9:00 TO 10:30AM EDT
Rice is the most consumed cereal in Senegal, accounting for 34% of total cereal consumption. Per capita consumption is 80-90kg annually, though there is an urban-rural divide. While domestic production has doubled between 2010-2021, it still only meets 40% of demand. As a result, Senegal imports around 1 million tons annually, mainly from India and Thailand. Several public policies aim to incentivize domestic production and stabilize prices, though rice remains highly exposed to international price shocks due to its importance in consumption and reliance on imports.
Abdullah Mamun and Joseph Glauber
IFPRI-AMIS SEMINAR SERIES
A Look at Global Rice Markets: Export Restrictions, El Niño, and Price Controls
Co-organized by IFPRI and Agricultural Market Information System (AMIS)
OCT 18, 2023 - 9:00 TO 10:30AM EDT
Shirley Mustafa
IFPRI-AMIS SEMINAR SERIES
A Look at Global Rice Markets: Export Restrictions, El Niño, and Price Controls
Co-organized by IFPRI and Agricultural Market Information System (AMIS)
OCT 18, 2023 - 9:00 TO 10:30AM EDT
Joseph Glauber
IFPRI-AMIS SEMINAR SERIES
A Look at Global Rice Markets: Export Restrictions, El Niño, and Price Controls
Co-organized by IFPRI and Agricultural Market Information System (AMIS)
OCT 18, 2023 - 9:00 TO 10:30AM EDT
This document provides an overview of the Political Economy and Policy Analysis (PEPA) Sourcebook virtual book launch. It summarizes the purpose and features of the PEPA Sourcebook, which is a guide for generating evidence to inform national food, land, and water policies and strategies. The Sourcebook includes frameworks, analytical tools, case studies, and step-by-step guidance for conducting political economy and policy analysis. It aims to address the current fragmentation in approaches and lack of external validity by integrating different frameworks and methods into a single resource. The launch event highlighted example frameworks and case studies from the Sourcebook that focus on various policy domains like food and nutrition, land, and climate and ecology.
- Rice exports from Myanmar have exceeded 2 million tons per year since 2019-2020, except for 2020-2021 during the peak of the pandemic. Exports through seaports now account for around 80% of total exports.
- Domestic rice prices in Myanmar have closely tracked Thai export prices, suggesting strong linkages between domestic and international markets.
- Simulations of a 10% decrease in rice productivity and a 0.4 million ton increase in exports in 2022-2023 resulted in a 33% increase in domestic prices, a 5% fall in production, and a 10% drop in consumption, with poor households suffering the largest declines in rice consumption of 12-13%.
Bedru Balana, Research Fellow, IFPRI, presented these slides at the AAAE2023 Conference, Durban, South Africa, 18-21 September 2023. The authors acknowledged the contributions of CGIAR Initiative on National Policies and Strategies, Google, the International Rescue Committee, IFPRI, and USAID.
Sara McHattie
IFPRI-AMIS SEMINAR SERIES
Facilitating Anticipatory Action with Improved Early Warning Guidance
Co-organized by IFPRI and Agricultural Market Information System (AMIS)
SEP 26, 2023 - 9:00 TO 10:30AM EDT
More from International Food Policy Research Institute (IFPRI) (20)
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 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.
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!
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.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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).
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
1. Working with
the Dataverse Repository
A training provided to the Ethiopian Nutrition Information Platform for Nutrition
Yetimwork Habte,
International Food Policy Research Institute, Ethiopia Strategy Support Project
EPHI Training Center, Addis Ababa,
March 12, 2019
2. Content of the training
Will introduce you to the basics of Dataverse including:
oWhat is dataverse
oPreparing your submissions
oCreating account and logging in
oAccessing Dataverse pages
oHow to add a dataset to your project’s Dataverse page
oDetails about what metadata to include
oPermissions
3. What is dataverse
Dataverse is a free, credible, open access data repository system.
It is a container for datasets and documents.
Link here: https://dataverse.harvard.edu/
Created and managed by Harvard University
Dataverse is used by research institutions world wide
Provides an academic data citation for all published datasets
Improves public accessibility of datasets
promotes collaboration and reduces the number of emails sent!
It is also possible to privately share information with access/permission
setting (setting up private url).
4. Step 1: Prepare Your Submission and convert it in to required format
1.1. Data cleaning
Example: data cleaning
oWe have 5 survey datasets with many sections (and variables)
oTook me about 1 month/ more
Check for incorrect data entry and missing values
String vs numeric values (destring command or generate new numeric variable)
Renaming and labeling (rename var, label var)
Generating new variables (eg unique id, hh_size) (gen command)
5. 1.2. Preparing codebook
Variable name variable label data type value value label Comments
hhq_id Household ID numeric
zone Zone Code numeric 1 North Shewa
2 West Shewa
3 Sub-urban
4 Addis
sectiona_1 Id code of person living in the HH numeric
sectiona_5 Is this person a minda numeric 1 Yes
Minda is someone who works for the
household on agriculture or livestock and
lives in the household
2 No
sectiona_20 Do you fast numeric 1 Yes
2 No
sectiona_21
Name of fasting period observed
text
hh_size Household size numeric
This is generated by counting no. of
observation per HH
Is another time taking task
o Example Preparing codebook
6. Step 1….. Prepare your submission
1.3. Sampling strategy (update)
1.4. Survey questionnaire
1.5. Consent form (language used)
1.6. Other important documents (eg related publications and reports)
Once cleaned and stripped of personally identifiable information (PII), you should
convert the data in to required format.
- E.g STATA to excel (csv format/ comma delimited format)
The codebook, sampling strategy, survey questionnaire and consent form should also
be converted in to required format
7. Step 2: Create an Account
1. Click here to
see the sign up
page
2. Fill in the
required
information
and create
your
Dataverse
account
9. Step 4: Find your Institution’s Dataverse Page
Type e.g
“EPHI” in the
search bar to get
your institution’s
page as the first
esult, it will
show up in the
esults list once
t is created.
Ethiopian Public Health Institute (EPHI)
Ethiopian Public Health Institute (EPHI)
10. Step 5: Find Your Project’s Dataverse Page
Next, click on the
name of your
project
The National Information Platform for Nutrition (NIPN)
Harvard Dataverse > Ethiopian Public Health Institute
11. Step 5: Add a Dataset
National Information Platform for Nutrition (NIPN)
Harvard Dataverse > Ethiopian Public Health Institute > National Information Platform
for Nutrition (NIPN)
Click on the “add
data” button and
select
“new dataset”
12. Step 6: Fill in the Metadata (provide information about dataset(s))
Add the title
of the dataset
Tip: Your
Open Data
Plan can help!
Add the title of
the dataset
E.g. Data for
child nutrition
survey
National Information Platform for Nutrition (NIPN)
Harvard Dataverse > Ethiopian Public Health Institute > National Information Platform for Nutrition (NIPN)
Host Dataverse National Information Platform for Nutrition (NIPN)Host Dataverse National Information Platform for Nutrition (NIPN)
Fill in the
information of the
person responsible
for the dataset and
the submitter (if a
different person)
Add a detailed
description of
the dataset
answering
who, what,
when, where,
why, and how?
Add the date when
the dataset was
first created
13. Relevant Options: medicine health & life sciences; agricultural
sciences; or other
Keywords are used
by others to search
for your datasets in
Dataverse. Choose
strategically to make
the data easy to
search for
Keyword Guidelines:
Click on the plus (+)
button to add more
Keywords or “Terms”
Related: include the
title and the link of
the work produced
using this data, if
any.
Notes Guidelines:
In the “Notes” section, include answers
to the following:
1.If this data relate to living, human
subjects or not
2.If informed consent was obtained or
not
3. summary of the data analysis done
using this data (a paragraph is enough)
14. Step 7: Upload your Dataset
File Upload Guidelines:
1. Datasets
2. Codebooks
3. Survey protocols and instruments
4. Methodologies for data analysis
5. Any annotations describing data quality issues in the data
6. Consent form language
Datasets submission format: excel( .csv ), stata, spss, etc
Reminder: Be sure to save your dataset!
Tip: Metadata can be edited in Dataverse after a
dataset is submitted
15. Harvard Dataverse > Ethiopian Public Health Institute > National Information Platform for Nutrition (NIPN)
To add files, edit the
metadata or delete the
dataset, click edit and
then use the drop
down menuWhen you are ready to make the
dataset published click the publish
button
16. Harvard Dataverse > Ethiopian Public Health Institute > National Information Platform for Nutrition (NIPN) M&E Dataverse
Step 8: Permissions
Click Edit and then
Permissions to
change permission
settings for your
project Dataverse
page
17. National Information Platform for Nutrition (NIPN)
Harvard Dataverse > Ethiopian Public Health Institute > National Information Platform for Nutrition (NIPN) > Dataset permissions
To give members of your
research team the ability to
add and edit datasets
assign them the role of
admin by clicking "Assign
Roles to Users/Groups"
18. Admin - A person who has all permissions for dataverses, datasets, and files.
Contributor - For datasets, a person who can edit License + Terms, and then submit them for
review. Can not publish dataset.
Curator - For datasets, a person who can edit License + Terms, edit Permissions, and publish
datasets.
Draft
File Downloader - A person who can download a file (including unpublished files)
Member - A person who can view both unpublished dataverses and datasets, and download files..
Search for the
researchers username
and assign a role (keep
in mind the search is
case sensitive)