#open-research-knowledge-graph — Public Fediverse posts

Frauen in der Wissenschaft: Dr. Jennifer D’Souza

Die Blogreihe „Frauen in der Wissenschaft” stellt Frauen aus der TIB vor, die Einblicke in ihre Wege und ihre persönlichen Erfahrungen in der Wissenschaft geben. Dr. Jennifer D’Souza studierte Natural Language Processing an der University of Texas in Dallas, USA, und arbeitet jetzt als KI/NLP Group Lead an der TIB.

An der TIB führt sie die Forschungsgruppe „NLP for Scientific Information” im Rahmen des Open Research Knowledge Graph (ORKG) an, einer Plattform, die sich der besseren Strukturierung, Maschinenlesbarkeit und Zugänglichkeit wissenschaftlicher Kenntnisse widmet. Die Forschung der Gruppe untersucht, wie Sprachmodelle und andere KI-Technologien die Organisation wissenschaftlicher Kenntnisse unterstützen kann, und erforscht dabei auch die Bewertung und Grenzen generativer KI-Systeme.

In diesem Interview verrät sie, was sie an einer Karriere in der Wissenschaft als besonders bereichernd erachtet, welche Erkenntnisse sie auf ihrem Weg gewonnen hat und was sie sich für die nächste Generation von Wissenschaftlerinnen erhofft.

Was fasziniert dich an der Arbeit in der Wissenschaft?

Dazu fallen mir drei Dinge ein. Erstens bietet die Wissenschaft einen systematischen Ansatz, um Fragen zu untersuchen und die Welt um uns herum zu verstehen. Sie liefert klar definierte Methoden, um komplexe Probleme und die große Menge an Informationen, die ständig produziert wird, anzugehen. Dieser Aspekt ist eng mit meiner eigenen Arbeit verbunden, in der es um die Verarbeitung und Organisation wissenschaftlicher Kenntnisse geht.

Zweitens arbeite ich sehr gerne mit gleichgesinnten Menschen zusammen, damit wir gemeinsam neue Systeme und Ideen entwickeln. Durch Teamwork und den Austausch von Perspektiven werden viele wissenschaftliche Fortschritte erzielt.

Drittens schätze ich die Möglichkeit, wissenschaftliche Ideen an die nächste Generation zu vermitteln und zu kommunizieren. Sein Wissen weiterzugeben und anderen dabei zu helfen, ihr eigenes wissenschaftliches Denken zu entwickeln, ist ein besonders bereichernder Aspekt wissenschaftlicher Arbeit.

Was hättest du als Frau in der Wissenschaft gerne früher gewusst?

Die Bedeutung des Aufbaus eines Forschungsnetzwerks. Die Menschen messen dem Networking unterschiedliche Bedeutung bei, aber die interessantesten Projekte, an denen ich gearbeitet habe, entstanden durch Gespräche mit anderen – entweder bei Workshops, Konferenzen, Forschungsprojekten oder informellen Diskussionen.

Im Laufe der Zeit habe ich immer mehr zu schätzen gelernt, wie wertvoll es ist, mit der breiten wissenschaftlichen Community ins Gespräch zu kommen, Menschen zu treffen, die an ähnlichen Problemen arbeiten, und Ideen auszutauschen. Schon die einfache Teilnahme an Workshops und Veranstaltungen zu Themen, die einen interessieren, kann unerwartete Möglichkeiten für Zusammenarbeit und Lernen eröffnen.

Welchen Rat würdest du Mädchen und jungen Frauen geben, die eine wissenschaftliche Laufbahn anstreben?

Frauen neigen häufig dazu, zu sehr an sich selbst zu zweifeln. Ich stimme vielen anderen zu, die die Bedeutung von Selbstvertrauen hervorgehoben haben. Frauen sollten ihren Fähigkeiten vertrauen und anerkennen, dass sie genauso in die Wissenschaft gehören wie jeder andere auch.

Ich würde auch junge Frauen ermutigen, ihre Ideen offen zu teilen, sich an Diskussionen zu beteiligen, und nicht zu zögern, sich Gelegenheiten zu verschaffen, wenn es diese nicht schon gibt. Die Wissenschaft profitiert von verschiedensten Perspektiven und wir brauchen mehr Frauen, die sich in wichtigen wissenschaftlichen Diskussionen zu Wort melden.

Ein Wunsch für die Zukunft von Frauen und Mädchen in der Wissenschaft …

Ich würde sehr gerne in eine Zukunft blicken, in der Frauen, die in die Wissenschaft gehen, sich nicht länger am falschen Ort fühlen. Vor allem hoffe ich, dass bereits in den Schulklassen Mädchen und Jungen in ingenieurwissenschaftlichen und technischen Disziplinen ausgeglichener vertreten sind. Ich denke, auch das gehört zum Thema dazu. Die Wissenschaft zeigt ihre ganze Stärke, wenn sie die Diversität der gesamten Gesellschaft widerspiegelt und wenn talentierte Menschen unterschiedlichster Herkunft sich aufgefordert fühlen, mitzumachen.

Es freut mich außerordentlich, dass wir bereits in vielen Forschungsumgebungen Anzeichen dafür sehen können, darunter auch in der TIB, in der verschiedene Teams und Perspektiven immer mehr Teil der alltäglichen wissenschaftlichen Arbeit werden. Ich freue mich auf eine Zukunft, in der solch eine Diversität schlicht und einfach die Norm ist.

Frauen in der Wissenschaft – eine Blogreihe

In der Blogreihe „Frauen in der Wissenschaft“ werden Frauen an der TIB vorgestellt, die Einblicke in ihre wissenschaftlichen Wege, Rollenbilder und ihre Erfahrungen aus dem Arbeitsalltag geben. Sie alle teilen ihre Perspektive und ihre Wünsche für die Zukunft der Wissenschaft und ermutigen andere Frauen, ihren Platz selbstbewusst einzunehmen.

Women in Science: Dr Jennifer D’Souza

The blog series “Women in Science” introduces women from TIB who share insights into their careers and personal experiences in science. Dr Jennifer D’Souza studied Natural Language Processing at the University of Texas at Dallas, USA, and is now an AI/NLP research group lead at TIB.

At TIB, she leads the “NLP for Scientific Information” research group within the Open Research Knowledge Graph (ORKG), a platform dedicated to making scientific knowledge more structured, machine-actionable, and accessible. Her group’s research explores how language models and other AI techniques can support scientific knowledge organization, while also investigating the evaluation and limitations of generative AI systems.
In this interview, she shares what she finds most rewarding about a career in science, the lessons she has learned along the way, and her hopes for the next generation of women scientists.

What fascinates you about working in science?

Three aspects come to mind. First, science provides a systematic way to study questions and understand the world around us. It offers well-defined methods for engaging with complex problems and the vast amount of information that is constantly being produced.

This is closely connected to my own work on processing and organizing scientific knowledge.

Second, I enjoy collaborating with like-minded people to build new systems and ideas together. Many scientific advances emerge through teamwork and the exchange of perspectives.

Third, I value the opportunity to teach and communicate scientific ideas to the next generation. Sharing knowledge and helping others develop their own scientific thinking is a particularly rewarding aspect of academic work.

As a woman in science, what would you have liked to have known earlier?

The importance of building a research network. While the value people place on networking varies, many of the most interesting projects I have worked on originated through conversations with others – whether at workshops, conferences, research projects, or informal discussions.

Over time, I came to appreciate how valuable it is to engage with the broader scientific community, meet people working on related problems, and exchange ideas. Simply attending workshops and events in areas that interest you can open unexpected opportunities for collaboration and learning.

What advice would you give to girls and young women who are considering a career in science?

Women often tend to second-guess themselves more than they should. I agree with many others who have highlighted the importance of confidence. Women should trust in their abilities and recognize that they belong in science just as much as anyone else.

I would also encourage young women to share their ideas openly, engage in discussions, and not hesitate to create opportunities for themselves when they do not already exist. Science benefits from diverse perspectives, and we need more women contributing their voices to important scientific conversations.

A wish for the future of women and girls in science …

I would love to see a future in which women entering science no longer feel like they are exceptions in the room. In particular, I hope to see much more balanced representation in engineering and technology-related disciplines, starting in our classrooms. At the same time, I do not see this as a separate issue. Science is strongest when it reflects the diversity of society as a whole and when talented people from different backgrounds feel welcome to contribute. One thing I particularly appreciate is that we are already beginning to see this in many research environments, including at TIB, where diverse teams and perspectives are increasingly part of everyday scientific work. I look forward to a future where such diversity is simply the norm.

Women in science – a blog series

The blog series “Women in Science” introduces women at TIB who provide insights into their scientific careers, role models and experiences from their everyday working lives. They all share their perspectives and wishes for the future of science and encourage other women to take their place with confidence.

“Papers with Code” went offline, the knowledge doesn’t have to

Launched in 2018, Papers with Code was a community-driven platform for exploring and discovering state-of-the-art research in artificial intelligence and machine learning. Within one year, it became a crucial infrastructure for the computer science community, growing into a resource with more than 18,000 papers and over 1,500 leaderboards (1). The platform aggregated studies from multiple sources and served as a central hub for benchmarked research in the form of leaderboards (see an example of a leaderboard here). It also supported the open science movement by publishing academic papers with their source code.

In response to this rapid growth, Papers with Code announced it was joining Facebook AI in 2019. Users were reassured that “Papers with Code [would] remain a neutral, open and free resource” and there would be no changes to their services. Yet, earlier this year, the Papers with Code website suddenly went offline. Without prior notice, users were simply redirected to the Papers with Code Github repository, and the machine learning community was left to wonder about the fate of this key resource.

About one month after the platform disappeared, Hugging Face, a private company providing collaborative platforms for machine learning models, released a LinkedIn announcement that it was building a successor platform in partnership with Papers with Code and Meta (formerly Facebook). While the Hugging Face website allows the research community to follow trending papers linked to their source code, it has only recently integrated leaderboard functionality. Unlike Papers with Code, which curated paper-centric leaderboards for a wide range of tasks, Hugging Face leaderboards focus on model-centric, reproducible evaluation pipelines such as the Open LLM Leaderboard, enabling users to compare deployed models under standardized conditions. One reason Papers with Code scaled so well is that it allowed researchers to submit performance results from any model (as reported in their papers), regardless of where the model was hosted. In contrast, Hugging Face’s current leaderboard setup requires the model to be publicly hosted on the Hugging Face Hub, loadable via supported APIs. This excludes many works that report results but do not deploy models in that way, limiting visibility into progress across all research.

The role of public institutions in safeguarding research data

The Open Research Knowledge Graph (ORKG) is an open-source and open-data project at the TIB-Leibniz Information Centre for Science and Technology that also enables benchmarked tracking of state-of-the-art research through comparisons (see Fig. 1). As a national library and foundation of public law under the German state of Lower Saxony, the TIB’s mission is to ensure sustainable access to information and digital data of high public value.

In 2021, the ORKG imported data from Papers with Code, capturing benchmarks that would have been lost if the Papers with Code website had gone offline (see Fig. 2). This highlights the importance of redundancy across digital infrastructures. If benchmarks are available only on commercial platforms, they remain vulnerable to corporate decisions, shifting business models, or sudden shutdowns. Public infrastructures, such as the ORKG, ensure that this knowledge remains accessible over the long term. This is a crucial example of the role public institutions play in providing continuity, safeguarding scientific knowledge, and ensuring that resources developed by and for the community do not simply disappear.

A call to the community

Continuity also requires participation. The strength of public infrastructures, such as the ORKG, depends on the level of community engagement. Keeping leaderboards populated with the latest benchmarks requires researchers to contribute their results. Here is our call to action: if you were disappointed to see Papers with Code discontinued, consider contributing your papers to the ORKG. Your contributions ensure the leaderboards tracking progress in your field remain open and accessible to everyone.

About the ORKG

The Open Research Knowledge Graph (ORKG) is a service that aims to revolutionise the way scientific knowledge is shared and used. By creating a structured, searchable knowledge graph, the ORKG makes scientific information more accessible and usable for the global research community.

Reference

(1) https://medium.com/paperswithcode/papers-with-code-is-joining-facebook-ai-90b51055f694

#LizenzCCBY40INT #OpenScience #OpenResearchKnowledgeGraph #PapersWithCode

“Papers with Code” went offline, the knowledge doesn’t have to

Launched in 2018, Papers with Code was a community-driven platform for exploring and discovering state-of-the-art research in artificial intelligence and machine learning. Within one year, it became a crucial infrastructure for the computer science community, growing into a resource with more than 18,000 papers and over 1,500 leaderboards (1). The platform aggregated studies from multiple sources and served as a central hub for benchmarked research in the form of leaderboards (see an example of a leaderboard here). It also supported the open science movement by publishing academic papers with their source code.

In response to this rapid growth, Papers with Code announced it was joining Facebook AI in 2019. Users were reassured that “Papers with Code [would] remain a neutral, open and free resource” and there would be no changes to their services. Yet, earlier this year, the Papers with Code website suddenly went offline. Without prior notice, users were simply redirected to the Papers with Code Github repository, and the machine learning community was left to wonder about the fate of this key resource.

About one month after the platform disappeared, Hugging Face, a private company providing collaborative platforms for machine learning models, released a LinkedIn announcement that it was building a successor platform in partnership with Papers with Code and Meta (formerly Facebook). While the Hugging Face website allows the research community to follow trending papers linked to their source code, it has only recently integrated leaderboard functionality. Unlike Papers with Code, which curated paper-centric leaderboards for a wide range of tasks, Hugging Face leaderboards focus on model-centric, reproducible evaluation pipelines such as the Open LLM Leaderboard, enabling users to compare deployed models under standardized conditions. One reason Papers with Code scaled so well is that it allowed researchers to submit performance results from any model (as reported in their papers), regardless of where the model was hosted. In contrast, Hugging Face’s current leaderboard setup requires the model to be publicly hosted on the Hugging Face Hub, loadable via supported APIs. This excludes many works that report results but do not deploy models in that way, limiting visibility into progress across all research.

The role of public institutions in safeguarding research data

The Open Research Knowledge Graph (ORKG) is an open-source and open-data project at the TIB-Leibniz Information Centre for Science and Technology that also enables benchmarked tracking of state-of-the-art research through comparisons (see Fig. 1). As a national library and foundation of public law under the German state of Lower Saxony, the TIB’s mission is to ensure sustainable access to information and digital data of high public value.

In 2021, the ORKG imported data from Papers with Code, capturing benchmarks that would have been lost if the Papers with Code website had gone offline (see Fig. 2). This highlights the importance of redundancy across digital infrastructures. If benchmarks are available only on commercial platforms, they remain vulnerable to corporate decisions, shifting business models, or sudden shutdowns. Public infrastructures, such as the ORKG, ensure that this knowledge remains accessible over the long term. This is a crucial example of the role public institutions play in providing continuity, safeguarding scientific knowledge, and ensuring that resources developed by and for the community do not simply disappear.

A call to the community

Continuity also requires participation. The strength of public infrastructures, such as the ORKG, depends on the level of community engagement. Keeping leaderboards populated with the latest benchmarks requires researchers to contribute their results. Here is our call to action: if you were disappointed to see Papers with Code discontinued, consider contributing your papers to the ORKG. Your contributions ensure the leaderboards tracking progress in your field remain open and accessible to everyone.

About the ORKG

The Open Research Knowledge Graph (ORKG) is a service that aims to revolutionise the way scientific knowledge is shared and used. By creating a structured, searchable knowledge graph, the ORKG makes scientific information more accessible and usable for the global research community.

Reference

(1) https://medium.com/paperswithcode/papers-with-code-is-joining-facebook-ai-90b51055f694

#LizenzCCBY40INT #OpenScience #OpenResearchKnowledgeGraph #PapersWithCode

"Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph"
https://doi.org/10.3390/info15060328
#LLM #OpenResearchKnowledgeGraph #ORKG

"Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph"
https://doi.org/10.3390/info15060328
#LLM #OpenResearchKnowledgeGraph #ORKG