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Open Science Information Infrastructure

Research on open, reproducible, and sovereign scientific information systems

We build open and reproducible ways to organize, connect, preserve, and make use of openly accessible scientific information.

Our research spans mathematical knowledge, scholarly knowledge graphs, research software, and sovereign digital infrastructure.

We work specifically with scientific information that is openly available and can be inspected, copied, processed, and redistributed without access restrictions.

This limitation is fundamental to our technical approach. It allows us to investigate transparent, distributed, and content-addressed infrastructure, including technologies such as IPFS, without introducing access-control mechanisms that would undermine openness, reproducibility, or independent verification.

Our work does not generally concern confidential or access-restricted information, sensitive information, or information containing personally identifiable information, or PII.

By concentrating on open scientific information, we aim to create workflows in which the source material, software, processing steps, and results can be independently examined and reproduced.

Our mission

Scientific knowledge increasingly depends on digital infrastructure.

Search systems, repositories, knowledge graphs, software archives, and computational services influence:

what information can be discovered;
how research results can be verified;
whether data and software can be reused;
how knowledge is connected and preserved;
who controls access to the scholarly record;
and who can participate in maintaining it.

Open science, therefore, requires more than openly accessible publications.

It requires information infrastructure that research communities can inspect, operate, adapt, govern, and preserve.

We aim to develop and strengthen infrastructure that is:

based on open standards;
implemented through inspectable and reusable software;
built around openly available scientific information;
designed for reproducible information processing;
interoperable across institutions and systems;
and governed in the public interest.

Our aim is not to create another isolated platform.

We work on components, standards, information models, and connections that support a diverse, interoperable open-science ecosystem.

Machine-actionable mathematics

We investigate how mathematical expressions, concepts, proofs, and documents can be represented for both human and machine use.

Research topics include:

mathematical information retrieval;
formula search;
semantic enrichment of mathematical documents;
recognition and disambiguation of mathematical entities;
connections between formulas and their meanings;
machine-readable representations of mathematical knowledge;
links between mathematical literature, software, and research data.

Mathematical notation is a particularly challenging form of scientific information.

The meaning of a symbol often depends on its surrounding document, discipline, and local context.

Making mathematics machine-actionable, therefore, requires more than extracting visual formula structures.

Scholarly knowledge graphs

We study how publications, researchers, institutions, software, datasets, and mathematical objects can be connected through open knowledge graphs.

Our goal is not simply to produce more metadata.

We aim to create reliable, inspectable, and verifiable paths through the scholarly record.

Relevant topics include:

persistent identifiers;
entity linking;
provenance;
metadata interoperability;
Wikidata and other open knowledge bases;
links between literature, software, and research data;
federated queries across independent information sources;
reconciliation between different identifier systems.

Open research software

Software is a first-class research output.

We work on methods and infrastructure for:

software discovery;
software metadata;
software citation;
source-code preservation;
links between software and publications;
connections to long-term archives such as Software Heritage;
recognition of software contributions in scholarly communication;
reproducible software environments;
open software registries and knowledge graphs.

Research software is both a scholarly output and a dependency of other research outputs.

Preserving a publication without preserving the software needed to reproduce it may leave an incomplete scholarly record.

Reproducibility and provenance

Computational research depends on more than a publication.

It may also depend on source code, input information, software environments, external services, and undocumented processing steps.

We explore ways to preserve and communicate this context through:

executable workflows;
computational notebooks;
dependency information;
structured provenance;
links between claims and supporting artifacts;
reproducible research environments;
versioned information pipelines;
content-based identification of research objects.

Responsible artificial intelligence for scientific information

Retrieval systems, recommendation systems, and language models increasingly mediate access to scientific knowledge.

We investigate how such systems can support research without becoming opaque gatekeepers.

Important questions include:

Which sources support an answer or recommendation?
How was the information selected and transformed?
Can users inspect the limitations of the system?
Can the system be independently evaluated?
Can communities operate or replace their components?
Are generated statements connected to verifiable sources?
Can the underlying information and processing steps be reproduced?
Does the system depend on inaccessible or proprietary information?

We are particularly interested in systems whose outputs can be traced back to open sources and independently verified.

Federated and decentralized infrastructure

We explore architectures that allow institutions and communities to cooperate without surrendering control to a single central platform.

Relevant concepts include:

federation;
decentralized identifiers;
content-addressed storage;
distributed knowledge graphs;
community-operated services;
synchronization between independent repositories;
portable identity and attribution mechanisms;
peer-to-peer distribution;
independently verifiable information objects.

Technologies such as IPFS are relevant because they identify content by its cryptographic representation rather than only by the location of a particular server.

This can support:

verifiable references to specific versions of information;
replication across independent hosts;
resilience against the disappearance of a single service;
reproducible access to exact information objects;
separation between the identity of information and the provider currently serving it.

Content-addressed systems do not by themselves solve questions of governance, discoverability, preservation, or scholarly quality.

We therefore study them as components within a wider information infrastructure rather than as complete solutions.

People

Our work takes place in and around the Mathematical Information Infrastructure department at FIZ Karlsruhe – Leibniz Institute for Information Infrastructure.

The group also collaborates with universities, libraries, archives, open-source projects, Wikimedia communities, and national and international research-infrastructure initiatives.

Moritz Schubotz

Dr. Moritz Schubotz is a computer scientist and theoretical physicist working on information infrastructure for mathematics and open science.

He heads Research and Projects Mathematics at FIZ Karlsruhe and teaches at Humboldt University of Berlin.

His research connects:

mathematical information retrieval;
semantic technologies;
scholarly knowledge graphs;
research software;
Wikimedia;
decentralized systems;
open-science infrastructure.

Researcher

Research interests:

Mathematical information retrieval, natural-language processing, machine learning, and mathematical physics.

Profile:

FIZ Karlsruhe profile

Principles

Open by default

We prefer:

open-source software;
openly documented interfaces;
open standards;
openly licensed metadata;
reproducible methods;
publicly accessible research outputs.

Our focus is not on opening information that should remain restricted.

Instead, we design infrastructure around scientific information that is already suitable for open use, redistribution, and independent processing.

Community control

Infrastructure should serve scholarly communities rather than capture them.

Researchers and public-interest institutions should be able to influence:

governance;
technical priorities;
information policies;
preservation strategies;
interoperability requirements;
and the conditions under which infrastructure components are operated.

Interoperability rather than isolation

No single repository, knowledge graph, or service can represent all of science.

We therefore work towards systems that can exchange information while retaining their institutional and technical independence.

Interoperability should not require all participants to use the same software or depend on the same provider.

Replaceable components

A service should not become indispensable merely because its information cannot be exported or its interfaces cannot be reproduced.

Where possible, infrastructure should support:

documented information exports;
standard protocols;
modular architectures;
independent implementations;
migration between providers;
local or community-operated instances;
reproducible reconstruction of derived information.

Verifiable information

Scientific information systems should expose their sources and transformations.

Users should be able to distinguish between:

original research outputs;
curated metadata;
automatically extracted information;
algorithmic recommendations;
model-generated summaries;
human corrections and annotations;
derived information produced by reproducible workflows.

Long-term stewardship

Maintenance is part of research infrastructure.

Documentation, testing, migration, curation, preservation, and governance are not secondary activities.

They determine whether scientific information remains usable after a project or funding period ends.

Decentralization where useful

Decentralization is not an end in itself.

We consider decentralized or federated technologies where they improve:

independent verification;
resilience;
portability;
reproducibility;
institutional autonomy;
long-term accessibility.

A decentralized system can still reproduce centralized power through governance, discovery services, or the control of key software components.

Technical distribution must therefore be considered together with institutional and community governance.

Projects and ecosystems

Mathematical knowledge infrastructure

We work on services and methods related to:

zbMATH Open;
mathematical search;
machine-actionable formulas;
mathematical classifications;
semantic enrichment;
links between mathematical literature, software, and research data.

Mathematical Research Data Initiative

The Mathematical Research Data Initiative, or MaRDI, develops infrastructure and standards for mathematical research data.

Our interests include:

the MaRDI Portal;
mathematical knowledge graphs;
metadata standards;
links between publications, software, and datasets;
reusable research workflows;
support for FAIR mathematical research data.

Research software infrastructure

We contribute to efforts involving:

swMATH;
software metadata;
software citation;
source-code discovery;
Software Heritage;
links between research software and the scholarly literature;
open registries of research software;
persistent and verifiable references to source code.

Open scholarly communication

We support approaches to scholarly communication based on:

open access;
community governance;
transparent evaluation;
reusable metadata;
open licensing;
sustainable public infrastructure;
verifiable links between claims and sources.

National and European infrastructure

Our work connects to broader initiatives such as:

the German National Research Data Infrastructure;
the European Open Science Cloud;
disciplinary and interdisciplinary research-data initiatives;
international persistent-identifier and metadata communities.

A useful practical test for infrastructure sovereignty is:

Can a research community understand the system, copy and export its information, replace a component, operate an independent instance, and continue its work if a provider or project disappears?

Software and demonstrators

RelaX

RelaX is an interactive relational-algebra calculator for teaching, learning, and experimentation.

Further resources

How we work

Start from public value. Infrastructure should serve research communities and society.
Work with open scientific information. Our technical approach assumes that the information may be copied, inspected, and redistributed.
Prefer open standards. Interfaces and information models should be documented and reusable.
Use replaceable components. No individual service should become an unavoidable dependency.
Treat maintenance as infrastructure work. Documentation, testing, and migration are part of the scholarly contribution.
Design for provenance. Users should be able to determine the source of information.
Support pluralism. Different communities may need different interfaces, governance models, and technical arrangements.
Connect rather than capture. We contribute to shared ecosystems and avoid creating new information silos.
Use decentralization selectively. Distributed technologies should address concrete problems rather than being adopted for their own sake.
Consider environmental and social costs. Sustainable infrastructure must account for computation, energy, labor, and long-term stewardship.

Work with us

We welcome collaboration with:

researchers;
students;
libraries;
archives;
infrastructure providers;
open-source communities;
Wikimedia communities;
scholarly publishers;
standards organizations.

Possible forms of collaboration include:

joint research projects;
infrastructure development;
Bachelor's and Master's theses;
doctoral research;
open-source software development;
software maintenance;
dataset and benchmark creation;
interoperability experiments;
seminars and workshops;
community-governance and policy work.

Projects should generally align with our focus on open scientific information and reproducible information workflows.

For inquiries, contact contact@schubotz.org.

News and updates

Further information