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Life Sciences
Molecular biology, genetics, cell physiology, algal biotechnology and laboratory-based experimental research.
Research · Data · Software
Yusuf Adni Yavuz
I work at the intersection of life sciences, scientific data analysis and research software — translating complex experimental data into reproducible workflows, interactive tools and understandable results.
Scientific ComputingComputational BiologyAlgal BiotechnologyInteractive VisualisationResearch Software
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Connected profile
An interdisciplinary research profile
The orbiting fields show how my work connects life sciences, experimental research, scientific data analysis and research software. Select a field to inspect it, or click the core to return to this overview.
Profile
A research profile built around connections.
My strongest work emerges where biological questions, data structures and software development meet. I particularly value careful documentation, reproducible analysis and tools that make complex scientific information easier to explore.
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Research Software
R, Shiny, SQL, reproducible reports, data pipelines and interactive scientific applications.
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Scientific Analysis
Statistical reasoning, data visualisation, structured interpretation and transparent communication of limitations.
Research interests
Questions I want to explore.
Click a field to reveal how it connects to my current experience and future direction.
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Algal Biotechnology
Cultivation, physiology and bioenergy applications of microalgae.
My experience includes cultivation experiments, photosynthetic measurements, biochemical analyses, anaerobic digestion and environmental-data integration.
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Interactive · tap to expand
R
Scientific Computing
Reproducible and scalable data workflows for research.
I develop R-based pipelines, Shiny applications, database workflows and automated reports for biological and large-scale datasets.
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Interactive Visualisation
Interfaces that make complex measurements understandable.
I am interested in dashboards and exploratory tools that connect raw data, statistical results and scientific interpretation.
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DNA
Functional Genomics
Understanding biological function through molecular and computational evidence.
My academic background includes genetics, genome research, molecular biology and functional-genomics coursework and laboratory practice.
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Experimental Statistics
Choosing analyses that respect the data and the research question.
I work with descriptive statistics, assumptions, parametric and non-parametric tests, visual diagnostics and reproducible reporting.
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Laboratory Digitalisation
Connecting measurements, documentation and analysis.
I am interested in systems that reduce manual transfer, improve traceability and support collaborative scientific workflows.
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Scientific competency map
Knowledge, practice and application.
This map distinguishes between skills applied independently, practised in projects or laboratories, studied through coursework, and areas still being developed. It reflects actual experience rather than examination outcomes alone.
Data & Software
Scientific analysis, application development and reproducible data infrastructure.
AppliedPractisedStudied
Data cleaning and transformationStructured workflows for research and large datasets
AppliedStatistical analysisDescriptive, parametric and non-parametric methods
AppliedData visualisationExploratory and publication-oriented graphics
AppliedR ShinyReactive interfaces, uploads, analysis and exports
AppliedHTML & CSSResponsive interfaces and portfolio development
PractisedJavaScriptClient-side interaction and UI behaviour
DevelopingPostgreSQL / SQLStructured storage, queries and application integration
AppliedChunk processing & ParquetMemory-aware processing of very large files
AppliedAPIs and banking interfacesConceptual and implementation-oriented exploration
DevelopingR Markdown / QuartoAutomated reports and transparent analyses
AppliedGit-based documentationVersioned work, issues and technical notes
AppliedLinux deploymentShiny Server, PostgreSQL and web infrastructure
PractisedAlgal Biotechnology
Experimental work from cultivation and physiology to biochemical composition and bioenergy.
AppliedPractised
Microalgal cultivationBag cultures, photobioreactor contexts and culture maintenance
PractisedOptical density & cell countsGrowth monitoring and comparison between conditions
AppliedBTM and OTMBiomass and organic dry matter determination
PractisedPAM fluorometryPhotosynthetic performance measurements
PractisedFv/Fm, ΦPSII and NPQMaximum/effective yield and non-photochemical quenching
AppliedChlorophyll contentPigment-related biochemical analysis
PractisedProtein contentDetermination and interpretation of biomass composition
PractisedN and P assimilation/contentNutrient uptake and biomass nutrient status
PractisedCondition comparisonIntegration of biochemical and growth measurements
AppliedAnaerobic digestionFermentation of algal biomass for biogas production
PractisedGas chromatographyQuantification of methane production and yield
PractisedWeather-station dataTemperature, UV index, daylight and environmental yield variables
AppliedMolecular Life Sciences
Coursework and laboratory foundations in genetics, cell biology, physiology and genomics.
Eukaryotic geneticsInheritance, gene regulation and molecular mechanisms
StudiedFunctional genomicsConnecting genome-scale data with biological function
StudiedGenome research laboratoryLaboratory work and scientific protocol completed
PractisedCellular organisation and transport
StudiedPhotosynthesis and respiration
StudiedPhysiological regulation
StudiedDNA, RNA and gene expression
StudiedMolecular laboratory workflows
PractisedScientific protocols and interpretation
PractisedChemistry
Foundational study and laboratory exposure across general, organic and physical chemistry.
Stoichiometry and equilibria
StudiedAcid–base and redox concepts
StudiedGeneral chemistry laboratory practice
PractisedFunctional groups and reaction principles
StudiedStructure–reactivity relationships
StudiedThermodynamic foundations
StudiedReaction kinetics and physical models
StudiedResearch Practice
From experimental documentation to manuscript preparation and transparent analysis.
First-author manuscript preparationScientific paper initiated in algal biotechnology
AppliedCo-author contributionSecondary-author contribution to a further manuscript
AppliedProtocols and reportsLaboratory, project and technical documentation
AppliedExperimental data capture
AppliedQuality and plausibility checks
AppliedTransparent limitations and open questions
AppliedIntroductory statistics seminar
StudiedBiological data interpretation
AppliedVisual and narrative communication
AppliedIntegrated workflow
From culture to scientific conclusion.
An interactive overview of how my laboratory, analytical and software skills connect in one research process.
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Cultivation⌁
Sampling⚗
Laboratory◎
Measurements⌗
DataΣ
Statistics▦
Software✎
Publication01 · Cultivation
Establishing biological systems
Maintaining algal cultures and comparing experimental conditions provides the biological foundation for the workflow.
Culture maintenanceGrowth mediaNutrient conditionsPhotobioreactor contextExperimental planning
02 · Sampling
Consistent collection and traceability
Sampling links time, treatment, culture and downstream measurements. Clear identifiers and documentation are essential.
Sampling schedulesSample identifiersLaboratory recordsTime-series structure
03 · Laboratory
Preparing biological material
Laboratory work includes biomass, pigment, protein and nutrient-related analyses as well as anaerobic digestion experiments.
BTM / OTMChlorophyllProteinN / P contentAnaerobic digestion
04 · Measurements
Capturing physiological and environmental signals
Experimental observations combine biological, biochemical, gas-chromatographic and weather-station measurements.
OD680 / OD750Cell countFv/FmΦPSIINPQMethane yieldTemperatureUV indexDaylight
05 · Data
Structuring heterogeneous observations
Laboratory and environmental records are cleaned, harmonised and prepared for reproducible analysis.
RData cleaningLong / wide formatsPostgreSQLValidation
06 · Statistics
Testing patterns responsibly
Analyses are selected according to the research question, distributions and assumptions, with limitations documented openly.
Descriptive statisticsNormality checkst / Welch testsANOVAKruskal–WallisCorrelations
07 · Software
Turning analyses into reusable tools
Interactive applications can connect uploads, databases, statistics, visualisation and automated reports.
ShinyInteractive dashboardsAutomated reportingCSV exportDatabase integration
08 · Publication
Communicating methods and findings
Scientific writing completes the workflow through interpretation, figure preparation and manuscript development.
First-author manuscriptCo-author contributionFiguresMethodsDiscussion
Featured research
Algal biotechnology as an integrated data problem.
Experimental research & scientific computing
Systematic Algal Biotechnology
My work combined cultivation, physiological and biochemical measurements, bioenergy experiments, environmental monitoring and automated data analysis. This created a multidisciplinary workflow extending from biological material to reproducible interpretation.
GrowthOD, cell counts, BTM, OTM
PhysiologyFv/Fm, ΦPSII, NPQ
BiochemistryChlorophyll, protein, N, P
BioenergyDigestion, methane, GC
EnvironmentTemperature, UV, daylight
ComputationR, automation, visualisation
First-author manuscript
Initiated as lead author within the context of algal-biotechnology research. The work involves scientific structuring, interpretation and manuscript development.
Secondary-author manuscript
Contribution as a co-author to a further scientific manuscript, supporting research documentation, analysis or interpretation.
Research software & live projects
Tools that turn complex work into structured workflows.
Two applications are publicly accessible and can be explored directly. They demonstrate how I combine scientific data analysis, structured documentation and accessible workflow design in R Shiny.
01 / BIOLOGICAL RESEARCH DATA
Interactive Algal Data Analysis Application
A research-oriented Shiny application for exploring, processing and visualising experimental algal data. It demonstrates the direction of my bachelor project: bringing laboratory measurements, statistical analysis and understandable visual outputs into one reproducible interface.
RShinyBiological dataInteractive visualisationStatistics
Role: Concept, development and scientific data workflow
yiceanalytics.de/PraktikumApp/
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The live Shiny app is embedded only when requested, preventing unnecessary loading time on the portfolio page.
02 / ACCESSIBLE ADMINISTRATIVE WORKFLOWS
LWL Documentation & Planning Tool
A structured planning and documentation application developed to break a complex personal-budget and assistance workflow into manageable areas, tasks and records. It is a practical example of software designed around real accessibility needs rather than around an abstract technical exercise.
R ShinyTask planningStructured documentationAccessibility-oriented designAdministrative workflows
Purpose: Systematically organise responsibilities, documentation and next actions connected to an LWL personal-budget process
yiceanalytics.de/open-participation-research-planner/
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Data Science Lab Internship Work
During my six-month internship at the Bertelsmann Stiftung Data Science Lab, I worked with large governmental datasets and developed R-based analytical and data-processing workflows.
RLarge datasetsParquetData qualityTechnical documentation
◈ Confidential work · NDA protectedProject data, source code, internal methods and visual examples cannot be published. This portfolio therefore describes only transferable skills and general technical responsibilities without exposing confidential material.
Municipality Explorer
A concept for an interactive, spatially oriented municipal-data platform combining structured indicators, maps and exploratory analysis.
R ShinySpatial dataInteractive mapsDashboards
Experience
Research, data and practical problem-solving.
Data Science Internship
Six-month internship, extended from an initial three months, involving large governmental datasets and R-based analytical workflows.
- Developed and documented R scripts for large-scale data processing.
- Investigated resource-efficient chunk and Parquet workflows.
- Communicated progress, limitations and open technical issues transparently.
- Actively requested feedback and incorporated suggestions into subsequent work.
◈ Work examples unavailable due to NDA
Laboratory Research & Scientific Data Analysis
Experimental and computational work spanning algal cultivation, physiological and biochemical measurement, environmental records and automated analysis.
- Completed laboratory, project and documentation components across biology, genetics and genome-research contexts.
- Developed automated analysis and visualisation workflows for project data.
- Initiated a first-author manuscript and contributed as co-author to another paper.
Customer Service and Bicycle Workshop Support
Practical service experience involving customer communication, retail operations, logistics and technical bicycle-related tasks.
Cognitive & professional profile
How I learn, solve problems and contribute in practice.
This section separates personal reflection on learning and cognitive working patterns from documented professional assessments. Together, they show both how I process complex information and how those abilities have been reflected in structured workplace reviews.
Cognitive profile
Self-reflection based on long-term interdisciplinary learning, scientific work and practical software development.
Professional profile
Combined scores from structured self-assessment and team-leader questionnaires discussed in individual, mid-year and annual review meetings.
How I learn
I learn most effectively by understanding the underlying principles of a system rather than memorising isolated steps. Once the conceptual structure is clear, I can usually reconstruct details independently and transfer the same reasoning to unfamiliar problems.
Rapid conceptual learning
A single well-structured explanation is usually enough for me to understand a new concept and begin applying it independently.
Strength: fast comprehension
Systems thinking
I naturally look for relationships, dependencies and underlying mechanisms instead of treating information as disconnected facts.
Strength: structural understanding
Pattern recognition and transfer
I connect recurring structures across biology, chemistry, statistics, data science and software development, making it easier to apply knowledge across disciplines.
Strength: interdisciplinary transfer
Independent exploration
Once the foundations are understood, I usually continue through literature, experimentation and implementation without needing repeated instruction.
Strength: self-directed learningDocumented professional assessment
The professional radar summarises recurring employee-development assessments completed through self-evaluation and team-leader evaluation. The final scores reflect the combined result discussed during individual, mid-year and annual review meetings.
Learning ability and adaptability
New tasks and unfamiliar procedures were understood quickly and transferred into practical work with minimal repeated guidance.
Documented score: 10/10
Problem-solving and initiative
Challenges were approached proactively, systematically and with a strong focus on finding workable solutions.
Documented score: 10/10
Constructive use of support
Independent completion was rated 9/10 because I appropriately involve colleagues when a task cannot be resolved alone rather than concealing uncertainty or risking lower-quality outcomes.
Documented score: 9/10These scores represent structured workplace review outcomes rather than newly invented portfolio ratings.
Preferred working environment
My strongest contributions emerge when I can investigate a defined analytical problem in depth and build a carefully documented solution.
Protected focus and clear priorities
Defined tasks, fewer interruptions and written priorities help me turn deep concentration into reliable results.
Helpful support: prioritisation
Explicit communication
Clear expectations, advance notice and structured feedback reduce unnecessary ambiguity and cognitive load.
Helpful support: clarityHow to read the diagrams
The cognitive radar combines high learning and reasoning strengths with lower values in areas affected by rapid context switching or simultaneous demands. Lower values indicate where structure or collaboration can improve performance; they are not hidden weaknesses.
The professional radar uses the original 1–10 workplace review scale. All listed areas received a combined score of 10 except independent completion, which received 9 because I ask for help appropriately when a problem cannot be resolved alone.
Contact
Let’s discuss research, data or scientific software.
For collaboration, project enquiries or future opportunities, the best way to reach me is by email.