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#reusability

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The first controlled ocean 🌊 #landing of a #Falcon9 booster was completed in 📆 April 2014. This was followed a little over a year later by the first successful recovery of a Falcon 9 booster on a ground pad in 📆 December 2015.

So, #reusable ♻️ launch technologies were most certainly available during the design and early development phases of #Ariane6 🇪🇺 europeanspaceflight.com/europe

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To get enough fuel ⛽ into #orbit for a #Mars 🔴 mission would require at least 10 launches of the #SLS rocket, or about $20 billion 💰. Just for the fuel. To use traditional propulsion, one needs to push the boundaries of #reuse ♻️ and heavy lift rockets to extreme limits—which is precisely what #SpaceX is trying to do with its fully reusable launch system arstechnica.com/science/2021/0

Ars Technica · Report: NASA’s only realistic path for humans on Mars is nuclear propulsionBy Eric Berger

The EU targets clothes and furniture in a crackdown on wasteful consumerism.

Textiles, furniture, tires and mattresses will be subject to much stricter design standards to ensure they last longer, as the EU aims to stamp out wasteful consumption, the European Commission confirmed on Wednesday.

mediafaro.org/article/20250416

A forklift in a textile recycling centre. | Leon Neal/Getty Images
Politico.eu · The EU targets clothes and furniture in a crackdown on wasteful consumerism.By James Fernyhough

Catching up on some bookmarked reading. This is worth dipping into for insights about #FAIR and (un)reusability of #dataset #metadata. The use case is #machlinelearning objects but transferrable lessons here IMHO.

Toward Enhanced #Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist #Repositories doi.org/10.7191/jeslib.685 #OpenData #OpenResearch #FAIRdata

Journal of eScience LibrarianshipToward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist RepositoriesObjective: The rapidly increasing prevalence and application of machine learning (ML) across disciplines creates a pressing need to establish guidance for data curation professionals. However, we must first understand the characteristics of ML-related objects shared in generalist and specialist repositories and the extent to which repository metadata fields enable findability and reuse of ML objects. Methods: We used a combination of API queries and web scraping to retrieve metadata for ML objects in eight commonly used generalist and ML-specific data repositories. We assessed both metadata schema and characteristics of deposited ML objects, within the context of the widely adopted FAIR Principles. We also calculated summary statistics for properties of objects, including number of objects per year, dataset size, domains represented, and availability of related resources. Results: Generalist repositories excelled at providing provenance metadata, specifically unique identifiers, unambiguous citations, clear licenses, and related resources, while specialist repositories emphasized ML-specific descriptive metadata, such as number of attributes and instances and task type. In terms of object content, we noted a wide range of file formats, as well as licenses, all of which impact reusability. Conclusions: Generalist repositories will benefit from some of the practices adopted by specialists, and specialist repositories will benefit from adopting proven data curation practices of generalist repositories. A step forward for repositories will be to invest more into use of labels and persistent identifiers to improve workflow documentation, provenance, and related resource linking of ML objects, which will increase their findability, interoperability, and reusability.
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📊 #ESA 🇪🇺 and its member states forked over $4.4B 💰💰💰💰 to develop #Ariane6. Customer launches are unlikely to pay back development costs any time soon.

#SpaceX has invested over $5B 💰💰💰💰💰 in #Starship 🚀 R&D to date. Starship has proven its expendable capability, and the company is focusing on achieving full #reusability ♻️.

#NASA spent a dizzying $24B 💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰💰 developing #SLS
payloadspace.com/rocket-develo

Payload · Rocket Development Costs by Vehicle: Payload ResearchIn 2008, Falcon 1 became the first privately funded, fully liquid-fueled launch vehicle to reach orbit. Its development cost? Just $90M ($131M inflation adj.).

#Transparency, #reproducibility and #reusability are _actually_ hard work if they are not used as mere buzzwords in research.

As an example, here is a recent data publication with 10 #Jupyter notebooks as supplementary materials for a paper:
anonymous-peer12345.github.io/

... this has become a pretty standard scope for my publications lately. Still a lot.

The amount of work that is needed for this is often not seen in peer review or academia.

anonymous-peer123456.github.ioanonymous-peer123456.github.io