819 research outputs found
Machine learning in marine ecology: an overview of techniques and applications
Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio
Do-it-yourself instruments and data processing methods for developing marine citizen observatories
Water is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number
of ecosystems depend on the health of these oceans; their study and protection are necessary.
Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective
ways of obtaining and processing marine environmental data.
The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on
volunteer participation.
Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase
the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets.
We believe that citizen science based tools -open source software coupled with low-cost do-it-yourself hardware- can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education.
This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea
is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using
open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors.L’aigua és el recurs més important per la vida al planeta Terra, cobrint més del 70% de la seva superfÃcie. Els oceans representen més del 70% de tota l'aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d'ecosistemes depenen de la salut d'aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs perÃodes de temps i al llarg d’amples à rees geogrà fiques poden ser necessaris per avaluar la salut dels ecosistemes aquà tics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes més rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquà tics en particular, mitjançant l'ús de persones sense coneixement cientÃfic especÃfic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana -programari lliure juntament amb maquinari de baix cost i del tipus "fes-ho tu mateix" (do-it-yourself en anglès)- poden ajudar a apropar la ciència del camp oceanogrà fic als ciutadans. A mesura que el gran públic participa activament en l'anà lisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost "fes-ho tu mateix" s’apliquen de forma efectiva a la recerca oceanogrà fica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anà lisi de vÃdeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vÃdeo en un dispositiu in-situ de baix cost "fes-ho tu mateix" per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automà tic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost "fes-ho tu mateix" amb sensors òptics
Do-it-yourself instruments and data processing methods for developing marine citizen observatories
La consulta Ãntegra de la tesi, inclosos els articles no comunicats públicament per drets d'autor, es pot realitzar prèvia petició a l'Arxiu de la UPCWater is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number
of ecosystems depend on the health of these oceans; their study and protection are necessary.
Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective
ways of obtaining and processing marine environmental data.
The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on
volunteer participation.
Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase
the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets.
We believe that citizen science based tools -open source software coupled with low-cost do-it-yourself hardware- can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education.
This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea
is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using
open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors.L’aigua és el recurs més important per la vida al planeta Terra, cobrint més del 70% de la seva superfÃcie. Els oceans representen més del 70% de tota l'aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d'ecosistemes depenen de la salut d'aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs perÃodes de temps i al llarg d’amples à rees geogrà fiques poden ser necessaris per avaluar la salut dels ecosistemes aquà tics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes més rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquà tics en particular, mitjançant l'ús de persones sense coneixement cientÃfic especÃfic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana -programari lliure juntament amb maquinari de baix cost i del tipus "fes-ho tu mateix" (do-it-yourself en anglès)- poden ajudar a apropar la ciència del camp oceanogrà fic als ciutadans. A mesura que el gran públic participa activament en l'anà lisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost "fes-ho tu mateix" s’apliquen de forma efectiva a la recerca oceanogrà fica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anà lisi de vÃdeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vÃdeo en un dispositiu in-situ de baix cost "fes-ho tu mateix" per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automà tic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost "fes-ho tu mateix" amb sensors òptics.Postprint (published version
Machine learning in marine ecology: an overview of techniques and applications
Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets
Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms
Traditional taxonomic identification of planktonic organisms is based on light microscopy, which is both time-consuming and tedious. In response, novel ways of automated (machine) identification, such as flow cytometry, have been investigated over the last two decades. To improve the taxonomic resolution of particle analysis, recent developments have focused on "imaging-in-flow," i.e., the ability to acquire microscopic images of planktonic cells in a flow-through mode. Imaging-in-flow systems are traditionally based on classical brightfield microscopy and are faced with a number of issues that decrease the classification performance and accuracy (e. g., projection variance of cells, migration of cells out of the focus plane). Here, we demonstrate that a combination of digital holographic microscopy (DHM) with imaging-in-flow can improve the detection and classification of planktonic organisms. In addition to light intensity information, DHM provides quantitative phase information, which generates an additional and independent set of features that can be used in classification algorithms. Moreover, the capability of digitally refocusing greatly increases the depth of field, enables a more accurate focusing of cells, and reduces the effects of position variance. Nanoplanktonic organisms similar in shape were successfully classified from images captured with an off-axis DHM with partial coherence. Textural features based on DHM phase information proved more efficient in separating the three tested phytoplankton species compared with shape-based features or textural features based on light intensity. An overall classification score of 92.4% demonstrates the potential of holographic-based imaging-in-flow for similar looking organisms in the nanoplankton range
Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities
Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
publishedVersio
Deep learning-based diatom taxonomy on virtual slides
Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 2020;10(1): 14416
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