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Research Data Management

Exercise 4 README file:

  • In a group of 3-4 people, identify a data set of interest, assign the researcher most familiar with the dataset to be the “contact person” to describe and answer questions regarding their research.

  • The other members of the group take turns to ask questions under the following headings and fill in the information.

  • As you are asking questions, consider your own dataset and how you would respond.

  • In case you don’t already have an ORCID, create one here.

  • Create a text file named README.txt, include information provided below, and deposit it in the parent directory of your project.

Group name:

Group Members Names/ORCID:

| What |

Title : What do you want to call your data set? e.g. “Type of experiment < project name>”, “Cell Migration assay in developing Zebrafish”

Description (abstract) : What is your project about? What is the aim? Hypothesis? How does this data relate to your project?

Keywords : Choose between 3-5 keywords that can help you search for your data. For instance, subject area, experiment type, main project name, animal mode, cell line, e.g. Neurological biochemistry, applied ecology, Imaging, Genomics, Zebra fish etc.

| Where |

Data Source: If you included someone else’s data in your data set, provide information on where it came from. It is preferable to include a permanent identifier like a DOI, or ORCID from the person you received the data from

Links: links where to find the data, where is stored & archived, any related data to it, DOIs to publications, repositories DOIs.

| Who |

Author information: Potentially the data collector or the main contact person. Indicate name, contact info and ORCID. e.g. John doe, MDC, Animal house unit, ORCID xxxx, email: johndoe@mdc-berlin.de

Contact person : Indicate the name and contact details (email and ORCID) of the person responsible for additional information. Is this person responsible for enforcing data retention and disposal? It can be the PI or group leader.

Collaborators: People involved in the project for instance, those involved in sample collection, processing, analysis and/or submission, Provide their roles if possible, names, contact info and affiliations.

Funder Information: Indicate funding agency, grant number, and principal investigator name, contact & affiliations.

Access rights: Who can view, edit and share the data? License information?

| When |

Temporal Coverage: When did the project start? When was the data collected? e.g. on a specific date? Specific time? Over a range of dates? For dates use the  ISO 8601 standard: YYYY-MM-DD. When was the data last updated?

Version: Are there other versions of this dataset? Where can they be found? Which version is this? e.g. Master version, published version.

Publication Date: When was the data published?

Archival & disposal: How long should the records be kept? Which data and file format will be kept? Who is authorized to access it, who is responsible for disposal decision and performance.

| How |

Method: Briefly describe the type of experiment, instruments used, information regarding frequency of data collection (e.g. time-point, over 24h), which samples were used? e.g. Live imaging zebrafish embryos over a period of 3 days using Zeiss Plan-Apochromat and Fiji software.

Data handling: How was the data controlled at various stages (data collection,digitisation or data entry, checking and analysis)? Which controls did you have in place? Did you produce or use any code/software to that purpose? Which and where can it be found? e.g. statistical comparison with p-values less than 0.01 were performed, scripts used can be found in GitHub repository + URL.

File index: Create a table of contents of folder structure and files included in the data set. Briefly describe the files present in each folder, naming conventions,codes and symbols, measurement units, abbreviations, file formats, data dictionaries, if applicable; list specialized softwares needed to open/use the files. e.g. Data dictionary: https://github.com/rreggiar/welcome-to-the-kim-lab/wiki/data-dictionary

Metadata: Description of the dataset in a reusable format such as .json & .xml to facilitate it’s discovery by humans and machines alike. For field specific metadata standards, please refer to the RDA Endorsed Recommendations.

| Resources |

| Attributions |

This template is adapted from the Mozilla Science Lab and contributors, original data available here.