VU template DMP with FER [id: 6]

I. Introduction

Thank you for participating in our study to explore the use of FAIR-Enabling Resources (FERs) as responses to Data Management Plan (DMP) questions. It will take 30-35 minutes to complete this DMP.

In this task, we will provide you with a DMP containing pre-populated answers to assist you in the task of finding the FER. The goal of this exercise is twofold:

1. You have the option to use the suggestions provided in the DMP. These suggestions are decisions made by research communities and can serve as valuable starting points for your responses.
2. Additionally, you should use the search bar to specify the FER. This demonstrates the efficiency and automation made possible by this machine-actionable feature. By using the search box, you can precisely locate the FER and make the result DMP more machine-processable.

This approach showcases the concept of machine-actionability, which is in contrast to the classical manual approach. Instead of solely relying on manual data stewardship, where all tasks are handled manually, you have the option to harness machine-assisted processes. You can either select responses from the provided suggestions or formulate your own answers. The key is to use the search bar effectively to pinpoint the FER, illustrating the enhanced efficiency and automation capabilities made possible by this feature.

Please make sure to keep track (or write it down on a piece of paper) of which questions you relied on the suggestions from FIPs when providing your answers. This information will be requested in the survey.

FAIR Data and Community Standards

Our main goal in this study is to see if using FERs as suggestions can make it easier for you to fill out DMPs. Additionally, we want to ensure that the data you generate follows the FAIR principles.

What is FAIR Data?

FAIR is an acronym that stands for Findable, Accessible, Interoperable, and Reusable.

• Findable: FAIR data should be easy to locate and access by both humans and computers. This entails assigning unique identifiers (such as DOIs or URLs) and clear metadata to data sets.
• Accessible: Once data is found, it should be readily accessible. This means that data should be stored in repositories that provide access to authorized users and ensure long-term preservation.
• Interoperable: FAIR data should be structured in a way that allows it to work smoothly with other data. Standards and common data formats play a crucial role in achieving interoperability.
• Reusable: To maximize the usefulness of data, it should be well-documented, explaining how others can use it. This involves providing comprehensive metadata and clear licensing terms for data reuse.

Community Standards and FAIRness

Community standards are essential in achieving FAIRness in data management. These standards are created collaboratively within specific research communities to ensure that data is collected, stored, and shared in a consistent and standardized way. When researchers follow these community standards, they help promote FAIR data practices within their field. In this study, we offer FERs from six social science communities to support this effort.

Study Overview

In this study, you will be presented with two distinct sections that encompass various aspects of data management and sharing within the social science domain. These sections will help us understand what you know, your views, and your choices when it comes to archiving and publishing data, documenting it, and managing resources for data.

Section 4: Data Archiving and Publishing (4 Questions)

In this section, you will find four questions related to data archiving and publishing. We will ask you about where and how you plan to publish your data. We are also interested in your choice of identifiers and licensing for your data assets.

Section 5: Documentation (3 Questions)

Section five focuses on the documentation needed for your project and data assets. We will inquire about metadata, methods, and tools necessary for accessing and using your data.

Feedback Survey

At the conclusion of your participation in this study, we kindly request you to provide us with your valuable feedback by filling out a short survey. Your feedback is instrumental in helping us understand your experience and gather insights that will assist us in refining our research methods and tools.

FAIR-Enabling Resources (FERs) Suggestions

To assist you in answering the questions in each section, we will provide suggestions for FAIR-Enabling Resources (FERs). These resources have been sourced from various FAIR Implementation Communities (FICs) in social science, which include:

1. SSHOC-NL Socio Economic History (SEH)
2. Media Content Analysis Lab (MCAL)
3. LGBTQ+ Linked Open Vocabulary (LGBTQVoC)
4. Social Science Survey Research (SSSR)
5. European Social Survey (ESS)
6. Australian Social Survey International-ESS (AUSSI-ESS)

These research communities were chosen because they were the only available ones and may be relevant to the researcher's DMP. These FERs are meant to serve as valuable references as you answer the questions in each section. You should both type your responses in the provided text fields and select relevant FERs from a dropdown menu for each question.

In case of trouble or questions, you can contact us on n.k2.singh@student.vu.nl.

II. 4. Data archiving and publishing

Please make sure to keep track (or write it down on a piece of paper) of which questions you relied on the suggestions from FIPs when providing your answers. This information will be requested in the survey.

III. 5. Documentation

Please make sure to keep track (or write it down on a piece of paper) of which questions you relied on the suggestions from FIPs when providing your answers. This information will be requested in the survey.

IV. Feedback Survey

We kindly request your participation in a brief survey designed to gather your feedback on the use of FAIR-Enabling Resource (FER) suggestions in Data Management Plans (DMPs). Your valuable input will help us understand the effectiveness of this approach and make improvements if necessary.

Time Estimate: This survey should take approximately 15 minutes to complete.

Number of Questions: There are a total of 12 questions in this survey.

Survey Link: https://vuamsterdam.eu.qualtrics.com/jfe/form/SV_4NtIaaPX6avzR3M

Your participation and insights are highly appreciated. Thank you for contributing to our research efforts.