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Dr. Heidi Seibold

My open science business - looking back after one year ๐Ÿ—“๏ธ

Published 3 months agoย โ€ขย 3 min read

I started full time self-employment on November 1st 2022. How did the first year go? Was it worth the trouble? What is coming next?

What an incredible year! I became unemployed in summer 2022 and decided to try self-employment. After various positions in academia and one in industry I was not only ready to be my own boss, but I was hungry to show the world that impact does not have to come through publishing papers.

How did the first year go?

All in all I am really happy with my life and my work. I have written in previous posts on how I define my "enough"-numbers and how I am working on not being everyone's good girl.

Becoming self-employed - it was worth it! Compared to my jobs before, I've got:

  • More freedom.
  • More impact.
  • More money? Not yet.

Freedom: I like being my own boss, setting my own agenda and driving forward the projects that I find useful. The only thing I missed was having a team. I mitigated that by working in an office next to other offices with NGOs, freelancers and companies. And now - with the Digital Research Academy - I am part of a team again.

Impact: I got to touch many lives this year. May that be through workshops, events, or this newsletter. It's hard to measure, but I feel like I did make more of an impact now that I used to. Just because I have the time to focus on the things that feel important.

Money: I had a fine salary in my previous two jobs. A little less than a professor would make. My revenue is about the same by now, but I've got bills to pay that I did not have before (office, other people, server, software, event participation, โ€ฆ). But (1) it's enough for a good life and (2) it will become more. I have learned that I need to learn to leave my comfort zone when asking for money. It's not a part of my job I enjoy, but I know that I deserve being paid according to my expertise. So I shall demand it.

One year, two themes

The first half of year 1 in self-employment I focused on learning the needed skills and setting myself up as a freelance trainer and consultant for open and reproducible data science.

The second half I started focusing on a new idea: building the Digital Research Academy, a trainer network for Open Science, Data Literacy, and Research Software Engineering.

It's crazy to see, how quickly the idea for the Digital Research Academy evolved. I had my first thoughts about it in the very first days of 2023, wrote them down and created a mini business plan. At the Open Science Retreat I had a few conversations about it and felt encouraged to write about the idea in a first post. It did not take long until we had first community meetings and the first two Train-the-Trainer (TTT) programs. Now we run regular trainer meetups and collaboration sessions and chat in the DRA Mattermost chat.

Looking back, it feels crazy how fast we set it up and how we are now 13 people already, who believe in the idea enough to put in time and love. Thanks to every single one of you. I love being part of this team. You're all amazing!

What's next?

2024 is approaching fast. So what are the plans? First, we'll run another Train-the-Trainer program, starting January.

Join the trainer community! ๐Ÿ™Œ

Digital Research Academy Train-the-Trainer program

January 24 - March 14 (participate once a week)

Application deadline: 15.12.2023

Also we - the Digital Research Academy - want to incorporate in early 2024.

The third big project that's up in early 2024 is the next Open Science Retreat, of course.

Open Science Retreat

March 25 - 29 in the Netherlands

Apply before 01.12.2023

There are a few other things cooking that I will write about another day.

Thanks for being part of this journey and for reading this newsletter!

Best,

Heidi


P.S. If you're enjoying this newsletter, please consider supporting my work by leaving a tip.

Heidi Seibold, MUCBOOK Clubhouse, Elsenheimerstr. 48, Munich, 81375
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Dr. Heidi Seibold

https://heidiseibold.com

All things open and reproducible data science.

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