Peter Cock<p><a href="https://fediscience.org/tags/ISMBECCB2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ISMBECCB2025</span></a> keynote Q&A: How will LLMs like ChatGPT affect the future of <a href="https://fediscience.org/tags/biocuration" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biocuration</span></a> ? I think Bairoch is hoping for domain specific integration with safeguards - like how <a href="https://fediscience.org/tags/TextMining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TextMining</span></a> was adopted - he stresses this is not a panacea and needs engineering work</p>
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For Immediate Release, April 1, 2025: University of Michigan Press will publish all of the content on Meta platforms as a series of printed books.
https://www.linkedin.com/posts/charles-watkinson-7553a257_amphibians-and-reptiles-of-the-great-lakes-activity-7312775744932179968-sLSu
Resulting from an @snsf_ch SPARK grant this took some time to mature, but the outcome is very imformative and builds a foundation for where to head next - how to liberate facts/information locked in the published literature #textmining #biodiversity https://preprints.arphahub.com/article/153174/
ARPHA PreprintsFrom literature to biodiversity data: mining arthropod organismal and ecological traits with machine learningThe fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from the literature. Testing and using such approaches to annotate articles in machine actionable formats is therefore necessary to enable the exploitation of existing knowledge in new biology, ecology, and evolution research. Here we explore the potential of these methods to annotate and extract organismal and ecological trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation, and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.
How does academic language shape a discipline? Daniel Erdmann @bbf_dipfberlin uses #TextMining to analyze the history of educational sciences in Germany after 1945—tracking language patterns, discourse shifts & conceptual trends in scholarly journals. 
#DigitalHumanities #rstats
Read more: https://href.hypotheses.org/4324 @NFDI4Memory @VHD
Our colleague Hidir Arras from patent4science research is co-organizing the 6th PatentSemTech Workshop at #SIGIR2025 in the beautiful city of Padua, Italy! Call for Papers is open 'til April 23: http://ifs.tuwien.ac.at/patentsemtech/
Submit your cutting-edge research, case studies, and demos exploring #AI, #NLP, and #TextMining innovations applied to #IP and related domains.
From Aachen to Zwickau: Mapping Correspondence in the Wiener Zeitung
At #Dhd2025 Nina C. Rastinger & Claudia Resch explore semi-automated methods for identifying correspondence locations in historical press research.
Key insights:
Transkribus enables tailored transcription with Field Model Training 21,793 headlines & 129,326 tokens analyzed Uneven network density: Strong news flow from London (enemy) & Paris (ally)
Automating Nature Detection in Historical Travelogues?
At #Dhd2025 Michela Vignoli & Doris Gruber (ONiT Project) explore how #LLM Llama 3.1 70B can analyze nature representations in multilingual travel reports
Challenges remain:
LLMs always produces results—even with flawed data LLM-corrected texts did not improve searchability in vector databases (3–14% drop)
Conclusion: LLMs aids discovery but manual review is essential for a reliable dataset.
[Atelier Data] Le lab INA organise un atelier @iscpif le 12 mars à 17h30 consacré à l’exploration (#statistique, #TAL…) de transcriptions de JT TF1 et FR2
Il reste encore quelques places : https://framaforms.org/atelier-donnees-ina-1739180738
Une certaine autonomie avec les outils d'analyse quantitative (Python ou R, CSV, etc.) est nécessaire afin de pouvoir profiter pleinement de l'atelier.
framaforms.orgAtelier données INA | Framaforms.org
Resulting from an Swiss National Science Foundation SNSF SPARK grant this took some time to mature, but the outcome is very imformative and builds a foundation for where to head next - how to liberate facts/information locked in the published literature #textmining #biodiversity https://www.biorxiv.org/content/10.1101/2025.02.18.638830v1
bioRxiv · From literature to biodiversity data: mining arthropod organismal and ecological traits with machine learningThe fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from the literature. Testing and using such approaches to annotate articles in machine actionable formats is therefore necessary to enable the exploitation of existing knowledge in new biology, ecology, and evolution research. Here we explore the potential of these methods to annotate and extract organismal and ecological trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation, and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.
### Competing Interest Statement
The authors have declared no competing interest.
Nouveaux outils pour la fouille de textes !
L'infrastructure Istex lance 2 nouveaux services puissants pour l'analyse de documents : Teeft : Extraction rapide des termes clés 
TermSuite : Extraction terminologique avancée
#TextMining #DataMining #ScienceOuverte
https://www.inist.fr/nos-actualites/nouveaux-outils-pour-la-fouille-de-textes-decouvrez-teeft-et-termsuite/
gen 
hi hivemind. let's say we want millions of unique IUPAC names from (organic chemistry) literature, how would you do this? Nothing else needed than the full, complete IUPAC name?