No waitlist. The work decides.

Write and Publish a Research Paper

3 milestones · 24 weeks

Conduct original research and publish a paper in a peer-reviewed journal or conference.

Complete a Measurable Environmental Action

3 milestones · 10 weeks

Lead or organise an environmental action with documented, quantified impact — trees planted, waste removed, land restored. Proof: before/after documentation and impact numbers.

Master the Times Tables

2 milestones · 6 weeks

Memorise and recall all multiplication tables from 1 to 12. A parent or teacher administers a timed quiz (2 minutes, all 144 facts) and confirms fluency. Photo of a completed quiz sheet is accepted proof.

Complete a Science Project

3 milestones · 8 weeks

Design and carry out a hands-on experiment — growing plants, testing materials, observing animals, or anything you're curious about. Share what you predicted, what happened, and what surprised you. A photo of the project is enough proof — no formal report required.

Write Your First Research Report

3 milestones · 8 weeks

Pick a topic that genuinely interests you and write a 3–5 page research report with at least 3 cited sources. The topic is entirely your choice. A teacher or parent confirms the report is complete and sources are cited. A document link or file is accepted proof.

Biology Experimental Design

0 milestones · 6 weeks

Design a real biological investigation from scratch: state a testable hypothesis, identify independent and dependent variables, define controls, justify sample size using a power analysis or reasoning from published literature, and document ethics considerations for living organisms or human participants. The proof is the complete experimental design document reviewed and approved by a working biologist BEFORE data collection begins — the biologist confirms that the design is methodologically sound and the hypothesis is genuinely testable. For students without lab access, the design may be for a secondary data analysis study using a named publicly available dataset (GBIF, NCBI SRA, iNaturalist) — the design document requirements are identical: hypothesis, variables, sampling methodology, statistical analysis plan. The design precedes data collection; this is a genuine prerequisite, not a formality.

Biology Data Collection and Statistical Analysis

0 milestones · 8 weeks

Execute a real biological investigation by collecting data from a controlled experiment OR from a named publicly available citizen science or genomics platform (iNaturalist, GBIF, NCBI GenBank/SRA) using a documented sampling methodology. The proof is the raw data with full metadata (location, time, conditions, collection method), a statistical analysis with appropriate tests (t-test, ANOVA, chi-squared, regression, or equivalent), and a written interpretation of results. Data collection from open platforms is not lesser proof — a documented GBIF biodiversity dataset with a rigorous sampling protocol and correct statistical analysis demonstrates the same analytical competency as lab-collected data. Reviewed by a biologist who examines both the raw data for methodological consistency and the statistical analysis for appropriate test selection. Fabricated data has characteristic distributional anomalies that a biologist reviewer detects — raw data submission is required.

Species Identification and Taxonomic Classification

0 milestones · 6 weeks

Identify and taxonomically classify at least 20 species from a defined habitat area using a documented dichotomous key or taxonomic reference. The proof is a species identification log with scientific name, common name, taxonomic classification (Kingdom through Species), habitat notes, and either a physical specimen, microscopy image, or high-quality photograph for each entry. For students without access to physical specimens or microscopy, iNaturalist photo observations with community verification and GPS documentation are accepted — but the taxonomic reasoning must still be documented using a named field guide or taxonomic key, not solely community identification. A biologist or naturalist reviews the log and confirms at least 10 identifications, challenging borderline cases and requiring the student to explain the distinguishing features used.

Biology Systematic Literature Review

0 milestones · 8 weeks

Conduct a systematic review of primary biological literature on a specific named biological question using PRISMA methodology: document your database search strategy (databases searched, search terms, date range), specify inclusion and exclusion criteria with rationale, and synthesise the findings — not merely summarise them. The proof is the completed review document including the PRISMA flowchart, data extraction table, and synthesis section that draws conclusions about the state of evidence for the named question. This step is fully accessible — PubMed, Google Scholar, and open-access journals (PLoS Biology, eLife, bioRxiv) are free and provide sufficient primary literature for any systematic review topic. Reviewed by a biologist who challenges your inclusion/exclusion decisions: specifically, why you included or excluded named studies in your results. Your documented methodology — not your summary conclusions — is what the reviewer assesses.

Biology Research Report (IMRaD Format)

0 milestones · 12 weeks

Write a complete research report in IMRaD format (Introduction, Methods, Results, Discussion, References) documenting a real biological investigation. The investigation can be original wet lab data OR a rigorous analysis of a named publicly available biological dataset (GBIF, NCBI SRA, iNaturalist) — both produce a real artifact with the same documentation requirements. The proof is the report plus a documented review by a working biologist who challenges the methodology section (specifically: are the controls adequate? is the statistical analysis appropriate? do the results support the conclusions?) and provides written feedback that the student must respond to. The Methods section must be detailed enough that another researcher could replicate the investigation. Primary literature (minimum 5 peer-reviewed sources) is required in the Introduction and Discussion.

Chemical Lab Safety and Risk Assessment

0 milestones · 4 weeks

Write a complete laboratory risk assessment for a real specific named chemical procedure — for example, 'acid-base titration of sodium hydroxide with hydrochloric acid' or 'recrystallisation of acetanilide from water.' A generic 'working in a chemistry lab' assessment is not accepted; the procedure must be specific and named. The assessment must identify the actual chemical hazards of the specific reagents and products (not generic lab hazards), control measures, required PPE with justification for each item, emergency procedures, and disposal protocols for the specific waste streams generated. Proof is the completed COSHH/MSDS-based risk assessment reviewed by a qualified chemist who confirms it correctly identifies the actual chemical risks of that specific procedure — a reviewer can immediately distinguish a real hazard assessment from a generic template. For students without lab access, the risk assessment may be written for a procedure to be performed as a secondary data analysis exercise using published data; the assessment must still document the real hazards of the specific named reagents involved.

Quantitative Chemical Analysis

0 milestones · 6 weeks

Perform a quantitative chemical analysis using a standard analytical technique: titration (acid-base, redox, complexometric, or precipitation), gravimetric analysis, or spectrophotometric quantification. The proof is the raw data (all trial results, not just concordant ones), calculations with error propagation, comparison to a reference or literature value with calculated percentage error, and a written discussion of systematic and random error sources. For students without lab access, the accessible alternative is a rigorous error propagation analysis using published reference analytical data from NIST Standard Reference Data or IUPAC certified reference values for a named compound — the mathematical analysis of precision, accuracy, and error sources demonstrates the same analytical reasoning as laboratory work. Reviewed by a chemist who examines the raw data for internal consistency and the error analysis for correct error propagation methodology.

Spectroscopic Data Interpretation

0 milestones · 6 weeks

Interpret real spectroscopic data — NMR (¹H, ¹³C, or DEPT), IR, or mass spectrometry — for a specific named compound. Document the reasoning for every peak assignment: chemical shift interpretation and multiplicity analysis (NMR), functional group identification (IR), or fragmentation pattern (MS). The proof is the annotated spectrum with documented reasoning for each assignment, a proposed or confirmed structure, and a written comparison to reference values. For students without lab access, authenticated spectra from SDBS (Spectral Database for Organic Compounds — free) or NIST Chemistry WebBook (free) are accepted — the interpretation methodology and documentation requirements are identical to lab-acquired data. Reviewed by a chemist who provides an unseen spectrum during the review session and asks you to assign it in real time — the live assignment is required to confirm you understand the methodology, not just the reference data.

Chemical Synthesis and Characterisation

0 milestones · 8 weeks

Plan and execute (or rigorously analyse) a chemical synthesis: document the procedure (reagents, quantities, reaction conditions, safety precautions), isolation methodology, percentage yield calculation, and full characterisation data (melting or boiling point, spectroscopic data including at least one of NMR, IR, or MS). The proof is the lab notebook record or equivalent documentation plus characterisation data with comparison to published literature values. For students without lab access, the accessible alternative is a rigorous comparative analysis of characterisation data from ChemSpider, Cambridge Crystallographic Data Centre (CCDC — free for academic use), or SDBS for a named literature compound — documenting each characterisation value, comparison to the reference, and what discrepancies would indicate about purity or identity. Reviewed by a chemist who challenges the characterisation interpretation, specifically asking about discrepancies between measured and reference values and what they indicate about the sample.

Chemistry Primary Literature Problem Solving

0 milestones · 6 weeks

Select a named primary chemistry paper (journal article, not a textbook) and work through a key calculation, derivation, or experimental result from that paper step by step. Document every step in the calculation or reasoning chain, identify approximations or assumptions made, and evaluate whether the methodology is sound. The proof is your documented step-by-step working for the selected problem plus a written assessment of the methodology's limitations. Primary chemistry papers are accessible via Royal Society of Chemistry open access, ACS Publications, PubMed, and arXiv chemistry preprints — entirely free. Reviewed by a chemist who presents a second related paper during the review session and asks you to identify the key calculation step — requiring you to demonstrate that you understand the methodology, not just the specific paper you prepared.

Mathematical Proof Construction

0 milestones · 8 weeks

Construct a complete formal mathematical proof in continuous mathematics — real analysis (limits, continuity, differentiation, integration), linear algebra (eigenvalues, vector spaces, matrix decomposition), differential equations (existence, uniqueness, stability), or probability theory (convergence, distributions, central limit theorem). The proof may be original or a reconstruction of a named non-trivial result with all steps made explicit and no reasoning omitted. Mathematical proof construction is an applied intellectual skill — the same reasoning that places legal argumentation in SKILLS rather than LEARNING. The skill is not knowing what a theorem says but demonstrating you can derive it from first principles with all logical steps documented. Mathematical proofs require no equipment — paper, pen, and primary mathematical texts are sufficient. Reviewed by a mathematician or physicist who confirms the proof is logically valid and complete; the reviewer presents an unseen claim during the review session for the student to work through live, confirming that the student can construct proofs rather than reproduce memorised ones.

Physics Experiment Execution and Analysis

0 milestones · 10 weeks

Design and execute a real physics experiment: state a specific testable prediction from physical theory, collect raw data with documented methodology and measurement uncertainty, perform error analysis (systematic and random errors, error propagation), and compare results to theoretical predictions with a quantitative assessment of agreement. The proof is the complete experimental record including raw data, error analysis, and a written conclusion discussing the comparison to theory. For students without lab access, analysis of a named real physics dataset from CERN Open Data Portal, NASA Exoplanet Archive, SDSS, or LIGO Open Science Center constitutes an equally valid proof — the methodology documentation requirements (data provenance, analysis steps, uncertainty quantification, comparison to theory or published values) are identical to lab work. Reviewed by a physicist who examines the error analysis methodology and asks how the results would change if a specific systematic error were introduced.

Computational Physics Modelling

0 milestones · 10 weeks

Build a real computational physics model or simulation using numerical methods — finite difference, Monte Carlo, molecular dynamics, or numerical integration of differential equations — implemented in code (Python with NumPy/SciPy, Julia, or equivalent). The proof is the submitted code (must be runnable as-is with documented dependencies), parameters with rationale, numerical outputs, visualisation of results, and a written validation section comparing the model output to an analytical solution or published reference value where one exists. Computational physics modelling is fully accessible — all tools are free and open source. Reviewed by a physicist who runs the submitted code independently to confirm it produces the documented outputs; the reviewer also asks you to modify a specific parameter and predict the outcome, then verify it during the review session.

Physics Open Data Analysis

0 milestones · 8 weeks

Analyse a named real publicly available physics dataset from a recognised source: CERN Open Data Portal, NASA Exoplanet Archive, IPAC Infrared Science Archive, Sloan Digital Sky Survey (SDSS), LIGO Open Science Center gravitational wave data, or equivalent. Document the full analysis methodology: data source and version, preprocessing steps, statistical analysis with appropriate tests, results with uncertainty quantification, and conclusions with physical interpretation. The proof is the analysis report plus the code or documented methodology sufficient for independent reproducibility. Real physics datasets contain real measurement noise, real systematic effects, and real signal-to-noise challenges — rigorous analysis of them produces artifacts of equal scientific value to lab-collected data. Reviewed by a physicist who challenges your interpretation of uncertainty in the results and asks whether alternative explanations for the observed pattern are consistent with the data.

Physics Derivation from Primary Literature

0 milestones · 8 weeks

Select a named primary physics paper (arXiv, Physical Review, Nature Physics, or equivalent) and work through a key derivation from that paper step by step — filling in all algebraic and physical reasoning steps that the authors compressed or omitted. Document every step with physical justification, identify approximations and their domain of validity, and write a brief assessment of what the derivation assumes and therefore cannot claim. This is fully accessible — arXiv (free), Physical Review Letters open access, and many Nature Physics papers are freely available. Reviewed by a physicist who selects a second related paper during the review session and asks you to identify the key step in a derivation you have not previously seen — requiring you to demonstrate that you understand the methodology, not just the specific paper you prepared.

Environmental Monitoring Data Collection

0 milestones · 6 weeks

Collect real environmental monitoring data from a specific named location using a documented methodology: air quality (PM2.5, NO₂, CO₂), water quality (pH, dissolved oxygen, turbidity, conductivity), biodiversity count, temperature, or equivalent environmental parameter. Document location coordinates, measurement dates and times, equipment or method used, and any data quality flags. For students without field access or equipment, monitoring data from named open government or citizen science networks is accepted: NOAA monitoring stations, EPA Air Quality System (free), OpenAQ (free), iNaturalist (for biodiversity), or Copernicus atmospheric data — but the provenance (station name, coordinates, data version) must be fully documented. The proof is a real documented dataset with full provenance — not a description of what you would measure, but actual data from an actual named location. Reviewed by an environmental scientist who examines the methodology for sampling bias and asks how the monitoring protocol would need to change to answer a specific environmental question.

Ecological Field Assessment

0 milestones · 6 weeks

Conduct a real ecological assessment of a specific named site using a standard methodology: point count or transect survey for bird species, quadrat sampling for plant communities, pitfall or sweep net survey for invertebrates, or equivalent standardised ecological method. Calculate at minimum one ecological index (species richness, Shannon diversity index, Simpson's index, or DAFOR abundance scale). The proof is the field survey record including site name, GPS coordinates or map reference, assessment date, methodology documentation, species list with counts or abundance scores, index calculation, and a brief written interpretation of the site's ecological condition. iNaturalist transect observations with documented methodology (defined transect, consistent effort, documented conditions) are accepted for students without access to ecological field equipment. Reviewed by an ecologist who challenges the choice of ecological index and asks how the assessment methodology would need to change for a different site type or ecological question.

Environmental Impact Analysis

0 milestones · 10 weeks

Conduct a real environmental impact analysis for a specific named development, land use change, or infrastructure project using established EIA methodology: baseline environmental conditions, impact identification (direct, indirect, cumulative), significance assessment (magnitude × likelihood), and mitigation recommendations. The proof is the completed analysis document for the named project plus a written statement of methodology and limitations. The project may be a real planned development (planning applications are publicly available from local authorities), a proposed policy change, or a historical project for which outcomes can be compared to predictions. Reviewed by an environmental scientist or EIA practitioner who challenges your significance assessment — specifically, whether you have correctly classified the magnitude and likelihood of named impacts — and asks whether a specific ecological receptor was adequately considered in your assessment.

Climate and Environmental Open Data Analysis

0 milestones · 8 weeks

Analyse a real named publicly available climate or environmental dataset: NOAA Global Surface Temperature, NASA GISS Surface Temperature Analysis, Copernicus Climate Change Service ERA5 reanalysis, EPA Air Quality System historical data, or equivalent. The proof is a documented analysis report: data source and version, preprocessing decisions with rationale, statistical analysis (trend detection, anomaly identification, spatial or temporal comparison), uncertainty quantification, and written conclusions with specific physical interpretation. All sources listed are free and publicly accessible. Real climate datasets contain real measurement uncertainty, coverage gaps, and confounds that require genuine scientific reasoning to navigate — rigorous analysis produces real artifacts of scientific value. Reviewed by an environmental scientist who challenges the statistical methodology — specifically asking whether the identified trend is robust to a specific alternative specification — and asks you to interpret a specific dataset feature you did not discuss in your report.