D4Dairy: Big Data for healthier cows - CSH Vienna

D4Dairy: Big Data for healthier cows


Aug 1, 2018

Dairy farming goes digital – and the Hub deals with the data

 

The Hub is going to get into cows now. How come? In June the Austrian Research Promotion Agency FFG–the national funding agency for industrial research and development in Austria–decided to support a project proposal put together by the Austrian Cattle Breeders’ Association (ZAR). The project, with the beautiful acronym “D4Dairy” (“Digitalisation, Data integration, Detection and Decision support in Dairying”) connects dozens of partners from agricultural organizations and farmers through industry and smaller size enterprises down to scientific institutions.

 

“In pilot studies, the ZAR has built up an infrastructure to measure and document all kinds of parameters in dairy farms,” explains Peter Klimek, key researcher for the Big Data analysis of the project. “They gathered huge amounts of data already, including environmental factors in stables, plus all veterinary diagnoses for two million cows or the genome sequences of 50,000 cows in Austria.” One of the industry partners, for instance, provides special sensors to be swallowed by the animals. Once in the rumen, the sensors constantly transmit information like changes in body temperature. This allows farmers or veterinarians to react early to a possibly beginning illness.

 

The stable as a “sensor landscape”

 

Data is where the Hub comes into play. First, data are–and will be–gained from diverse sources in different formats. The lack of communication between the systems does not only give a headache to farmers, who see themselves forced to double or multiple (manual) recording; diverging data sets make data analysis at a large scale difficult, too.

 

“Every company uses its own standards, and sends the data to different servers,” says Olga Saukh, the CSH–TU Graz key researcher for the “Digitalisation, data integration and decision support” part of D4Dairy. “As the systems are not plug’n’play, we have to integrate the data for further use: check for monocompliance, calibrate, structure the data gaining processes.” In addition, Olga will expand future data collection in a meaningful way to cover specific research questions defined by Peter and colleagues. In doing so, punctual measurements will increasingly be replaced by constant data streams.

 

For the well-being of cows – and humans

 

Peter, for his part, is looking forward to a new dimension of medical data analysis: “For humans, such comprehensive datasets are still missing,” he knows – one reason being data security concerns, “a minor problem with animals,” as he smilingly adds.

 

The major output of D4Dairy should be the improvement in the health and well-being of milk cows. From the perspective of complexity science though, Peter wants to come to a better understanding of the interplay between nature and nurture in general. Big Data, says the complexity expert, will help disentangle genetic, environmental and individual factors that contribute to either health or disease in a subject. “With 83 percent of their genes being identical to ours, cows are genetically quite close to humans,” he says. “The methods and prognostic models we are going to develop and test in D4Dairy will be applicable to human data as well.”

 

 

Farming, science, industry

 

The project D4Dairy was submitted by the Austrian Cattle Breeder’s Association ZAR within the framework of COMET (Competence Centers for Excellent Technologies).

 

COMET is an Austrian science program launched in 2006 to foster the cooperation and knowledge transfer between small, medium and large enterprises, universities, Universities of Applied Sciences, competence centers and research institutions.


Press

Regionale Lockerungen bald möglich


ORF, May 25, 2020

Press

Studie zeigt: Ärzte schützen Wien vor Virus-Drama


Kronen Zeitung, May 24, 2020

Press

Corona-Zahlen in Wien dank Ärztefunkdienst niedrig


ORF Radio Wien Nachrichten, May 24, 2020

Press

Studie: Wiener Maßnahmen funktionieren


ORF 2, May 23, 2020

Publication

F. Tria, I. Crimaldi, G. Aletti, V. D. P. Servedio

Taylor’s Law in Innovation Processes

Entropy 22(5) (2020) 573

News

May 15, 2020

Corona | How measures work [May]

News

May 7, 2020

Where did your last swallow’s nest come from?

Publication

C. Tsallis, U. Tirnakli

Predicting COVID-19 peaks around the world

(preprint)

News

Apr 30, 2020

Corona | How measures work [April]

News

Apr 29, 2020

Corona-Ampel | Einschätzung der Lage im Bezirk

Publication

P. Jizba, J. Korbel

When Shannon and Khinchin meet Shore and Johnson: Equivalence of information theory and statistical inference axiomatics

Phys. Rev. E 101 (2020) 042126

Publication

R. Entezari, O. Saukh

Class-dependent Compression of Deep Neural Networks

In Proc. of the International Workshop on Machine Learning on Edge in Sensor Systems (Sensys-ML @ CPS-IoT Week), 2020 (accepted)

Press

Regionale Lockerungen bald möglich


ORF, May 25, 2020

Press

Studie zeigt: Ärzte schützen Wien vor Virus-Drama


Kronen Zeitung, May 24, 2020

Press

Corona-Zahlen in Wien dank Ärztefunkdienst niedrig


ORF Radio Wien Nachrichten, May 24, 2020

Press

Studie: Wiener Maßnahmen funktionieren


ORF 2, May 23, 2020

Publication

F. Tria, I. Crimaldi, G. Aletti, V. D. P. Servedio

Taylor’s Law in Innovation Processes

Entropy 22(5) (2020) 573

News

May 15, 2020

Corona | How measures work [May]

News

May 7, 2020

Where did your last swallow’s nest come from?

Publication

C. Tsallis, U. Tirnakli

Predicting COVID-19 peaks around the world

(preprint)

News

Apr 30, 2020

Corona | How measures work [April]

News

Apr 29, 2020

Corona-Ampel | Einschätzung der Lage im Bezirk

Publication

P. Jizba, J. Korbel

When Shannon and Khinchin meet Shore and Johnson: Equivalence of information theory and statistical inference axiomatics

Phys. Rev. E 101 (2020) 042126

Publication

R. Entezari, O. Saukh

Class-dependent Compression of Deep Neural Networks

In Proc. of the International Workshop on Machine Learning on Edge in Sensor Systems (Sensys-ML @ CPS-IoT Week), 2020 (accepted)

News

May 15, 2020

Corona | How measures work [May]

News

May 7, 2020

Where did your last swallow’s nest come from?

News

Apr 30, 2020

Corona | How measures work [April]

News

Apr 29, 2020

Corona-Ampel | Einschätzung der Lage im Bezirk

News

Apr 29, 2020

International recognition for the CSH Covid-19 Control Strategies List [DE | EN]

News

Apr 24, 2020

CSH fordert besseren Datenzugang

News

Apr 20, 2020

Live tracker for Austrian Emotions [DE & EN]

News

Apr 14, 2020

Testing for Corona with Pool Size Calculator

News

Apr 10, 2020

Study: COVID-19 prevalence in Austria

News

Apr 9, 2020

Corona | Confirmed cases [April]

News

Apr 6, 2020

Check out our new CCCSL page!

News

Apr 3, 2020

CSH successful in WWTF COVID-19 call

Press

Regionale Lockerungen bald möglich


ORF, May 25, 2020

Press

Studie zeigt: Ärzte schützen Wien vor Virus-Drama


Kronen Zeitung, May 24, 2020

Press

Corona-Zahlen in Wien dank Ärztefunkdienst niedrig


ORF Radio Wien Nachrichten, May 24, 2020

Press

Studie: Wiener Maßnahmen funktionieren


ORF 2, May 23, 2020

Press

Forschung rettet Leben


Salzburger Nachrichten [print], May 22, 2020

Press

Wie Corona die Harmonie förderte


Die Presse, May 22, 2020

Press

HausärztInnen als Seismografen der Psyche


Hausarzt [print], May 22, 2020

Press

Neun von zehn Unternehmen der Mobilitätswirtschaft verzeichnen Umsatzrückgänge


APA OTS, May 20, 2020

Press

Corona-Forschung -Mit Mathematik gegen das Virus


ZDFheute, May 20, 2020

Press

Bilanz Intensivstation – Intensivmediziner warnen vor „Chamäleon“ Corona


Med online , May 20, 2020

Press

Corona und Mathematik: Das können wir aus den Zahlen lernen


ZDFheute, May 20, 2020

Press

Hintergrund: So funktionierten die Corona-Modellrechnungen


MedMedia, May 19, 2020

Publication

F. Tria, I. Crimaldi, G. Aletti, V. D. P. Servedio

Taylor’s Law in Innovation Processes

Entropy 22(5) (2020) 573

Publication

C. Tsallis, U. Tirnakli

Predicting COVID-19 peaks around the world

(preprint)

Publication

P. Jizba, J. Korbel

When Shannon and Khinchin meet Shore and Johnson: Equivalence of information theory and statistical inference axiomatics

Phys. Rev. E 101 (2020) 042126

Publication

R. Entezari, O. Saukh

Class-dependent Compression of Deep Neural Networks

In Proc. of the International Workshop on Machine Learning on Edge in Sensor Systems (Sensys-ML @ CPS-IoT Week), 2020 (accepted)

Publication

F. Schweitzer, T. Krivachy, D. Garcia

An Agent-Based Model of Opinion Polarization Driven by Emotions

Complexity (2020) 5282035

Publication

A. Pluchino, ..., V. Latora

A Novel Methodology for Epidemic Risk Assessment: the case of COVID-19 outbreak in Italy

[submitted]

Publication

S. Thurner ,W. Liu, P. Klimek, S. A. Cheong

The role of mainstreamness and interdisciplinarity for the relevance of scientific papers

PLOS ONE 15 (4) (2020) e0230325

Publication

R. Hanel, S. Thurner

Boosting test-efficiency by pooled testing strategies for SARS-CoV-2

[arXiv]

Publication

J. Korbel, R. Hanel, S. Thurner

Information geometry of scaling expansions of non-exponentially growing configuration spaces

Eur. Phys. J. Special Topics 229 (2020) 787–807

Publication

N. Haug, C. Deischinger, M. Gyimesi, A. Kautzky-Willer, S. Thurner, P. Klimek

High-risk multimorbidity patterns on the road to cardiovascular mortality

BMC Medicine Vol 18, Art 44 (2020)

Publication

A. Desvars-Larrive, S. Smith, G. Munimanda, et al.

Prevalence and risk factors of Leptospira infection in urban brown rats (Rattus norvegicus), Vienna, Austria

Urban Ecosyst (2020)

Publication

A. B. Migliano, F. Battiston, ... V. Latora, L. Vinicius,

Hunter-gatherer multilevel sociality accelerates cumulative cultural evolution

Science Advances Vol 6, No 9 (2020)