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Category: Tool Comparisons

  • Integrated PhD vs Traditional PhD in the UK: Which Route Should You Take? (2026)

    Integrated PhD vs Traditional PhD in the UK: Which Route Should You Take? (2026)

    The choice between an integrated doctorate and a direct-entry one is usually presented as “do you need a master’s first?”. That is the least interesting difference. The real distinctions are how long you are funded, who you spend the first year with, how many progression gates stand between you and the degree, and how much of your project is settled before you arrive. Comparison first.

    Direct-entry PhD Integrated PhD (MRes+PhD) CDT cohort programme Industry-embedded route
    Typical shape Straight into research One taught/research master’s year, then the doctorate Cohort training year, then the doctorate An industrial year plus the integrated programme
    Length 3 to 4 years; ESRC’s standard model is now 3.5 4 to 4.5 years 4 years 5 years in the Cambridge Industry+ model
    Entry requirement Usually a relevant master’s Designed for entrants without one Varies; often direct from a first degree As the CDT, plus eligibility to work in the UK
    Project defined at application Often, especially where you wrote the proposal Frequently chosen during the master’s year Usually chosen from offered projects after the training year Shaped by the industrial partner’s needs
    Progression gates Upgrade or confirmation review Master’s assessment, then the upgrade Training-year assessment, then the upgrade All of the above
    First year spent Largely alone, with your supervisors Partly in taught classes With a cohort, in structured training In a company graduate programme
    Best for A settled question and the training to pursue it Changing discipline, or no master’s yet Interdisciplinary fields; people who want a cohort Applied research and industry careers

    Start with a correction: “1+3” is no longer accurate everywhere

    The shorthand every applicant learns is “1+3” — one master’s year plus three doctoral years. In engineering and physical sciences it still describes reality. In the social sciences it is now out of date.

    The ESRC introduced a new PhD model from 2024/25, and its standard studentship is now 3.5 years. Doctoral Training Partnerships have relabelled accordingly: UBEL DTP, for instance, states that its ESRC studentships “are structured around the following routes: 1+3.5, +3.5, and +4.5 funding“, with the caveat that “not all routes are available in all institutions or pathways”. So the integrated social-science route is four and a half funded years, not four, and the direct-entry route is three and a half, not three.

    That extra half-year is not a bonus; it is a recognition of how long doctorates actually take. There is a further wrinkle: from October 2024 the ESRC made “Research in Practice” a core component, meaning all its funded students “should have the opportunity to complete a high-quality placement in academia, policy, business or civil society organisations as part of their training”. Where a placement is taken, additional funded time may follow — check your own DTP’s award structure page, because the routes on offer differ by institution and pathway.

    Note also what this does not establish. As our stipends and funding roundup records, UKRI publishes no sector-wide studentship duration — the length is devolved to the individual research council. ESRC’s 3.5 years is an ESRC number, not a UK one, and quoting it as though it covers EPSRC or AHRC is a mistake.

    Route 1: The direct-entry PhD

    You arrive with a master’s, or with equivalent research training, and begin your project in week one. The research years are the whole degree, punctuated by the upgrade or confirmation review and then the examination.

    Who it suits: anyone with a settled research question, the methods training to pursue it, and a supervisor already identified — frequently someone who wrote their own proposal, as covered in our guide to the PhD research proposal for a UK studentship.

    Where it falls short: the first year is the loneliest in UK doctoral education. There is no cohort by construction, no structured training unless your department provides it, and no institutional mechanism for discovering that your methods need work before you are committed to them. It is also the route with the least slack: if the project turns out to be undoable, you find out in year two with no taught year absorbed as a buffer.

    Route 2: The integrated PhD

    A one-year master’s — usually an MRes, sometimes a taught MSc or MA in research methods — followed by the doctorate, funded and administered as one award. The master’s year is not a separate hurdle you must reapply after; progression is normally automatic on satisfactory performance.

    Who it suits: three groups specifically. People without a master’s who would otherwise have to fund one privately. People changing discipline, for whom the taught year is genuine retraining rather than revision. And people whose question is a direction rather than a project — the master’s year is where a direction becomes a design.

    Where it falls short at doctoral scale: it is a year longer, which is a year of stipend-level earnings foregone against a graduate salary. It adds a progression gate. And if you already have a good master’s in the right field, the taught year can be genuinely redundant — funders know this, which is why “+3.5” exists as the alternative route within the same schemes.

    One administrative trap worth knowing: on integrated routes you often make two applications. UBEL’s guidance is explicit that 1+3.5 applicants “will need to make separate applications for both your Masters and doctoral programmes”, either simultaneously or during the master’s year.

    Two doctoral route timelines drawn in parallel, one with an additional first year
    The comparison is not three years against four. It is what the first year contains — and whether you would otherwise have paid for it yourself.

    Route 3: The CDT cohort programme

    Centres for Doctoral Training are the most structured route, and the one whose defining feature is other people. A CDT recruits a cohort each year and puts them through a shared training year before doctoral projects begin.

    Cambridge’s EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment is a representative example, describing its MRes+PhD as “a 1+3 model with a one year Masters course with taught and research elements followed by a three year PhD programme”. Its first year is dense and deliberately broad: an induction and a field trip to construction sites and sites of engineering or historical importance; three bespoke core taught modules covering theory, research methods and multidisciplinarity; two electives drawn from Engineering and related departments; an individual desktop study; a mini-project with a strong industry link; and a group project built on large-scale case studies such as Crossrail, High Speed 2 and the Severn Tidal Power project.

    Who it suits: researchers in interdisciplinary or applied fields, anyone who wants training breadth beyond their own project, and — not a small thing — anyone who suspects that four years of solitary work would not suit them. The cohort is a support structure the direct-entry route simply does not have.

    Where it falls short: less autonomy. Projects are typically chosen from a list shaped by the centre’s themes and its industrial partners, so if you arrive with a specific question of your own, a CDT may be the wrong container. Progression to the doctorate is also conditional on the training year, and the training year is real work.

    Route 4: The industry-embedded route

    The longest and least known option. Cambridge’s Industry+ scheme, run with Arup and Laing O’Rourke, has students “spend a year employed by one of these partners before coming to Cambridge to start their MRes+PhD programme” — a five-year programme of an industrial year, then one MRes year, then three PhD years. During the industrial year students are part of the company’s graduate training programme while remaining supported by the centre, visiting Cambridge to meet an advisor and take part in training. Applicants must be eligible to work in the UK.

    Who it suits: anyone whose career plan runs through industry rather than academia — which, on the destinations evidence, is most doctoral graduates. Only 41% of UK doctoral graduates were working in higher education fifteen months after graduating, and research roles outside universities are both growing and better paid than academic research posts; the figures are in our page on doctoral career destinations.

    Where it falls short: five years is a long commitment, the research agenda is shaped by a partner’s needs, and eligibility rules exclude some international applicants. Publication and confidentiality arrangements also need checking early if you intend a thesis by publication.

    Doctoral training cohort on an industrial site visit during a taught training year
    A structured first year buys training, a cohort and time to choose a project. It costs a year of autonomy and a year of earnings.

    The five questions that actually decide it

    1. Do you already have research training in the right methods? If yes, the taught year is redundant and the direct-entry route is the efficient choice. If no, someone has to pay for that training, and an integrated award means it is not you.
    2. Is your question a project or a direction? A project wants direct entry. A direction wants a year to become one.
    3. How well do you work alone? This is the question applicants weigh least and regret most. A CDT cohort is a structural answer to the isolation that the sector’s own experience data keeps surfacing.
    4. Where do you want to be at the end? Industry-embedded and CDT routes build networks in the direction most doctoral graduates actually go.
    5. What is the funded time, exactly? Not the advertised length — the funded length, in your council’s current model, for your specific route. Ask the DTP or centre directly, and get the answer in writing before you accept.

    The recommendation

    Take the direct-entry route if you have a relevant master’s and a settled question, because the taught year buys you nothing you do not already have. Take an integrated route if you lack the master’s, are switching field, or have a direction rather than a design — the extra year is funded training you would otherwise have to purchase. Take a CDT if your field is interdisciplinary or applied, or if you know that four years alone would not work for you. Take an industry-embedded route only if you actively want an industrial career, in which case it is the strongest option on this page and badly under-applied for.

    The wrong reason to choose any of them is speed. The difference between routes is one funded year, and the thing that actually determines when you finish is whether the writing keeps moving from year one — which is why the candidates who submit on time are the ones drafting chapters long before they feel ready to. Tesify keeps that structure and drafting moving across the whole candidature, whatever route you took: chapters, sections and an automatic bibliography in one workspace, 100% written by you.

    Frequently asked questions

    What is an integrated PhD in the UK?

    A funded programme combining a one-year research master’s with a doctorate as a single award, so you progress into the PhD without reapplying. It is designed for entrants who do not already hold a relevant master’s.

    What does “1+3” mean?

    One master’s year plus three doctoral years. It still describes many engineering and physical sciences CDTs accurately, but the ESRC’s standard model changed from 2024/25, so social science routes now appear as 1+3.5, +3.5 or +4.5.

    Is an integrated PhD worth an extra year?

    If you need the master’s, yes — it is funded training you would otherwise pay for. If you already have a relevant master’s, no, and the “+3.5” style route within the same scheme exists precisely for you.

    What is a CDT?

    A Centre for Doctoral Training: a funded centre recruiting an annual cohort into a structured programme, typically with a shared taught and training first year before doctoral projects begin. Cohort training and industrial engagement are the defining features.

    Do I choose my own project in a CDT?

    Usually from a list. Projects are shaped by the centre’s research themes and its industrial partners, and continuation to the PhD is normally conditional on the training year and on being accepted by a supervisor onto one of the offered projects.

    How long is an ESRC studentship now?

    The standard model is 3.5 years following the ESRC’s change from 2024/25, with 1+3.5 and +4.5 variants offered by DTPs. Routes vary by institution and pathway, so check your own DTP’s award structure.

    Do integrated PhD students get the same stipend?

    On a UKRI-funded integrated award, the stipend normally runs across the whole programme including the master’s year, at the standard rate. Confirm the funded duration and rate for your specific route before accepting.

    Can I switch routes once I have started?

    Rarely as a formal switch, since the funding is awarded against a route. What does happen is variation within a route — an extra placement period, a change of project, a suspension — all of which run through your DTP or centre rather than being informal.

    Is a CDT PhD viewed differently by employers or examiners?

    Examiners assess the thesis against the same doctoral criteria whatever route produced it. Employers, particularly in industry, often read cohort training and industrial engagement as a positive, which is part of the point of the model.

    Do integrated routes still have an upgrade review?

    Yes. The master’s or training year is an additional gate, not a replacement for the upgrade or confirmation review, which happens at the usual point in the doctoral phase.

    Are these routes open to international applicants?

    Many are, though eligibility and fee arrangements vary by scheme, and industry-embedded routes can carry a right-to-work requirement for the industrial year. Read the eligibility section of the specific advert rather than assuming a general rule.

  • R vs Python vs SPSS vs Stata for a Postgraduate Data Science Dissertation (2026)

    R vs Python vs SPSS vs Stata for a Postgraduate Data Science Dissertation (2026)

    The choice is usually made badly, and it is usually made once. Whatever you pick in month two is what you will still be using the week before submission, because nobody rewrites an analysis pipeline in year two of a research degree for aesthetic reasons. So the decision deserves twenty minutes of thought against the criteria that actually bite at postgraduate scale — licensing after you graduate, reproducibility when an examiner asks you to rerun something, and whether tables reach your manuscript without manual retyping.

    The comparison at a glance

    Criterion R Python SPSS Stata
    Cost to you Free, open source Free, open source Commercial; usually via university licence Commercial; student licences discounted
    Access after you graduate Unaffected Unaffected Lost when licence ends Perpetual licence available at extra cost
    Learning curve from zero Moderate to steep Moderate Shallow — menu driven Shallow to moderate
    Classical statistics coverage Comprehensive Good, less complete Comprehensive for standard tests Comprehensive, especially econometrics
    Machine learning Good (tidymodels, caret) Best in class Limited Limited
    Meta-analysis Excellent (metafor, meta) Weak Add-on required Strong (meta suite)
    Survey and complex sampling Strong (survey package) Weak Add-on module Excellent (svy commands)
    Reproducibility of a full analysis Excellent — script plus Quarto Excellent — script plus notebooks Poor if used through menus Very good — do-files and logs
    Publication-ready tables into the thesis Excellent Adequate Manual export Excellent
    Supervisor likely to know it Common in health and stats Common in computing Common in social sciences Common in economics and epidemiology

    The ranked shortlist

    1. R — the default for a postgraduate dissertation with a statistical core

    R wins the criteria that matter over a multi-year project rather than a single assignment. It costs nothing, so the analysis does not die when your student credentials expire. Its statistical coverage is the broadest of the four, and for anything methodologically specialised — mixed-effects models, survival analysis, meta-analysis, structural equation modelling — someone has already written and validated a package.

    The argument that clinches it for thesis writing is Quarto and R Markdown. You write a document in which the code and the prose live together, and the numbers in your results section are generated by the analysis rather than copied into it. When your supervisor asks what happens if you exclude the under-25s, you change one line and rerun the document. Every table and figure updates. Anyone who has spent an evening retyping a regression table after a late data correction understands why this is not a minor convenience.

    Where it falls short at postgraduate scale: the learning curve is real, and it is steepest in the first fortnight when you are also trying to make progress on the actual research. Error messages are unhelpful to beginners. Package quality varies, and a package with forty users may be abandoned before you submit — check maintenance activity before building a chapter on one.

    2. Python — correct if the dissertation is genuinely a machine learning project

    If your contribution involves model architectures, large or unstructured data, text or images, or anything you would describe as engineering rather than inference, Python is the right answer and R is not. The scikit-learn, PyTorch and transformers ecosystems have no serious equivalent elsewhere, and if your work needs to be deployed or handed to a collaborator it will be expected in Python.

    Python is also the better choice when the data acquisition is a substantial part of the project — scraping, APIs, database work and pipeline orchestration are far more natural here than in the alternatives.

    Where it falls short at postgraduate scale: classical inferential statistics are Python’s weak flank. Statsmodels is capable but thinner than R’s coverage, and for mixed models, complex survey designs or meta-analysis you will be working harder than you need to. Notebooks are also a reproducibility trap — cells run out of order produce results nobody, including you, can reconstruct six months later. If you use notebooks, restart and run all before believing any number.

    3. Stata — the quiet strong option for quantitative social science and epidemiology

    Stata is underrated by people who have never used it. Do-files give you genuine reproducibility with a fraction of R’s learning cost, the log file records exactly what was run, and the documentation is the best of the four by a wide margin — each command’s manual entry includes the underlying methods and references.

    For complex survey data, panel data and epidemiological analysis it is arguably better than R, because the survey and panel commands are built into the core language rather than assembled from packages. Table export into a manuscript is mature.

    Where it falls short at postgraduate scale: it costs money, and the version tied to your university licence stops working when you leave unless you buy a perpetual licence. Machine learning support is thin. And it holds one dataset in memory at a time by default, which is an awkward constraint for multi-source analytical projects.

    4. SPSS — usable, but hard to justify as a deliberate choice

    SPSS is fine for standard analyses — t-tests, ANOVA, regression, factor analysis — and its point-and-click interface means you can produce results in week one rather than week four. For a dissertation whose contribution is substantive rather than methodological, that is a legitimate trade.

    The problem is what happens afterwards. Menu-driven work leaves no record of what you did. Six months on, facing a correction that asks you to rerun an analysis with one variable recoded, you will not remember which dialogue boxes you ticked. The mitigation is to paste every command into a syntax file and run everything from syntax — which recovers reproducibility, but at that point you are writing scripts anyway and the main argument for SPSS has evaporated.

    Where it falls short at postgraduate scale: licence dependence, weak version control, limited modern methods, and output that has to be manually reformatted for every table in your thesis.

    The recommendation

    Use R unless you have a specific reason not to. It is free forever, statistically comprehensive, and it is the only option on this list whose document-generation story genuinely removes an entire category of late-stage errors from your thesis.

    Use Python instead if the dissertation’s contribution is machine learning, unstructured data or engineering. Use Stata if you work with complex survey or panel data and your supervisor uses it, because supervisor fluency is worth more than any feature comparison. Use SPSS only if your programme mandates it or your timeline genuinely cannot absorb a learning curve — and if you do, work entirely from syntax files.

    The criteria people forget until it is too late

    Can your supervisor debug it?

    A supervisor who uses your software can spot in ninety seconds an error that would cost you two days. This single factor outweighs most technical differences, and it is worth asking directly at your first methods meeting. It is one of the practical questions covered in running a supervisory relationship well.

    Will the analysis still run after your corrections?

    Assume you will be asked to rerun something after examination. A scripted analysis reruns; a menu-driven one has to be reconstructed from memory. This is a live concern rather than a theoretical one, since reanalysis requests are a routine part of completing corrections after a viva.

    Does the software match your sample size assumptions?

    Power calculations and sample size justification usually happen before you write a line of analysis code, and the tool you choose should support the design you committed to. If you have not fixed that yet, start with sample size conventions in postgraduate health research and choose software afterwards.

    Does it handle your synthesis method?

    If your dissertation includes an evidence synthesis component, meta-analysis package quality becomes a first-order criterion — and it is the one place R and Stata are clearly ahead. The workflow this feeds into is covered in writing a PRISMA systematic review chapter.

    Where does the output actually go?

    Whatever you choose, decide early how a regression table gets from the software into your manuscript, and whether that route survives your thesis being written in LaTeX or Word. The trade-offs are set out in the LaTeX and Word comparison for long theses.

    What about mixing them?

    Mixing is normal and usually fine. Cleaning a dataset in Python and modelling it in R is a common and defensible pattern. The rule is that every handoff must be scripted and the intermediate file versioned — an undocumented manual step in the middle of a pipeline is exactly the thing you will be unable to explain in a viva.

    Qualitative components sit outside all four of these tools; if your design is mixed-methods, you will need dedicated software alongside, and the comparison of NVivo, ATLAS.ti and Taguette covers the options including a free one.

    Keep the analysis and the writing in the same place

    The gap between a working analysis and a written results chapter is where most postgraduate time disappears — numbers copied by hand, tables rebuilt after a data correction, a methods section that no longer describes what the code does. Tesify keeps your drafts, sources and notes in one workspace so the chapter stays anchored to the analysis it describes, while the interpretation remains unmistakably yours.

    Start your dissertation with Tesify

    Frequently asked questions

    Is R or Python better for a data science dissertation?

    R is better if the contribution is statistical inference; Python is better if it is machine learning, unstructured data or engineering. For a dissertation that mixes both, choose the one matching your primary contribution and script the handoff to the other.

    Will examiners judge you for using SPSS?

    Not for the software itself. Examiners judge whether the analysis was appropriate and whether you can explain it. What does attract criticism is being unable to say precisely what was run — which is a risk of menu-driven work, not of SPSS as such.

    Do universities provide SPSS and Stata free?

    Many UK institutions hold site licences covering registered students, sometimes including home installation. Check your IT services pages before purchasing anything. Note that licences typically expire when your registration ends, which matters if you submit papers after graduating.

    How long does it take to learn R well enough for a dissertation?

    Enough to clean data and run standard models is a matter of weeks with consistent practice. Fluency takes longer. The efficient route is learning against your own dataset from the start rather than working through unrelated tutorial exercises.

    Should you use Jupyter notebooks for thesis analysis?

    They are excellent for exploration and risky as a final record, because out-of-order execution produces results that cannot be reproduced. If notebooks are your final artefact, always restart and run all before reporting any number, and keep the notebook under version control.

    Can you switch software partway through a dissertation?

    Possible but expensive, and the cost rises sharply once analysis is written up. If you are seriously considering a switch, do it before your main analysis begins. After that, the safer route is usually to add a second tool for a specific task rather than migrate everything.

    Which software is best for meta-analysis?

    R, using metafor or meta, is the most complete and is free. Stata’s meta suite is excellent and easier to learn. SPSS requires an add-on and Python’s support is thin, so neither is a natural choice for a synthesis-heavy dissertation.

    Do you need version control for dissertation analysis code?

    Strongly advisable. Git costs an afternoon to learn at a basic level and gives you a recoverable history of every analysis decision. At minimum, keep dated copies of scripts and never overwrite the version that produced results you have already written up.

  • Best AI Research Assistants for PhD Students in 2026: Discovery and Appraisal Tools Compared

    Best AI Research Assistants for PhD Students in 2026: Discovery and Appraisal Tools Compared

    Two distinct jobs hide inside “AI research assistant”, and buying the wrong one is how doctoral researchers end up paying a subscription they stop opening by December. Discovery tools help you find what to read, usually by walking the citation graph. Appraisal and extraction tools help you do something structured with papers you have already found. This comparison covers both, judged on what happens at 300 sources rather than at the onboarding demo. Table first.

    Tool Job Index / scale (publisher’s own figure) 2026 price Doctoral verdict
    Semantic Scholar Baseline search “over 200 million academic papers” Free (Allen Institute for AI) The floor everyone should be standing on
    ResearchRabbit Citation-graph discovery “over 310 million academic papers”; “1,000,000+ researchers” No pricing page published Best free discovery tool for exploratory reading
    Litmaps Discovery + monitoring Not published as a paper count Free tier (20 inputs, 2 maps, 100 articles/map); Pro $10/month, $120/year, academic email required Buy it for the alerts, not the maps
    Connected Papers Single-seed graph visualisation Not verifiable — see note Not verifiable — see note Useful for one job, not a subscription
    Elicit Extraction + screening “more than 138 million papers” Free tier; Plus $11/mo ($132/yr); Pro $39/mo ($468/yr); Scale $89/mo ($1,068/yr) The one paid tool most systematic reviewers should consider
    Scite Citation-context appraisal “1.6B+ citations”, “300M+ scholarly sources” No free tier — 7-day trial; Basic $20/mo, Pro $50/mo, billed yearly Field-dependent; buy only if contested claims are your problem

    Prices were read from each vendor’s own pricing page in August 2026 and are shown at the annual rate where one exists. Two verification notes, because a comparison that hides them is not much use. Connected Papers’ site returns “we’re sorry but Connected Papers doesn’t work properly without JavaScript enabled” to a plain fetch, and its pricing page carries no readable content — so nothing about its current tiers is quoted here rather than guessed. Consensus blocked the same check outright. Treat every price you read anywhere, including here, as needing a look at the vendor’s page before you enter a card.

    The shortlist, ranked for a doctorate

    1. Semantic Scholar — the free baseline

    Run by the Allen Institute for AI, Semantic Scholar describes itself as providing “free, AI-driven search and discovery tools, and open resources for the global research community”, indexing over 200 million papers from publisher partnerships, data providers and web crawls. It has no paywall, no tier and no upsell.

    Who it suits: everyone, as a starting point. Where it falls short: it is a search engine rather than a workflow — no screening, no extraction tables, no project structure. Use it as the layer beneath whatever else you adopt, and note that its open API is why several tools further down this list exist at all.

    2. ResearchRabbit — the best free discovery tool

    ResearchRabbit works the way exploratory reading actually works: start from a paper you trust, expand outward through related works, authors and citations, and let collections build as you go. Its own figures are “over 310 million academic papers” and “1,000,000+ researchers worldwide”. Notably, its site publishes no pricing page at all.

    Who it suits: anyone in the first eighteen months, or anyone entering an unfamiliar sub-literature. Where it falls short at doctoral scale: discovery without discipline sprawls. A graph will happily hand you four hundred papers, and nothing in the tool tells you which forty matter. Pair it with a hard inclusion rule you wrote down first.

    An abstract citation network with clustered nodes representing related academic papers
    Citation-graph tools are excellent at showing you a field’s shape and terrible at telling you where to stop. The stopping rule has to come from your protocol, not the visualisation.

    3. Litmaps — discovery plus the thing nobody budgets for

    Litmaps builds seeded maps of a literature and — the genuinely valuable part over a three-to-four-year candidature — monitors them, pushing alerts as new work appears. The free tier allows up to 20 inputs, 2 maps and 100 articles per map with monthly alerts; Pro is $10 a month or $120 a year with unlimited maps and daily or configurable alerts, and the education rate requires an academic email address.

    Who it suits: candidates past the upgrade, whose literature is defined and now needs to stay current until submission. Where it falls short: the free tier’s 100-article cap is small for a doctoral map, so this is effectively a paid tool — but at $120 a year it is the cheapest insurance against the examiner question “were you aware of the 2027 paper on this?”.

    4. Connected Papers — one job, done well

    Give it a seed paper and it produces a similarity graph of the surrounding literature — a fast orientation to an unfamiliar area, and genuinely useful the week you take on a new chapter. It is not a workflow, does not manage a project, and its current commercial terms could not be verified for this comparison. Use it as an occasional instrument rather than something you subscribe to and forget.

    5. Elicit — the extraction engine

    Elicit is the tool that does something other than find papers. Its free Basic tier already offers unlimited search across “more than 138 million papers”, unlimited summaries, chat with full-text papers and Zotero import — which is more than most candidates will exhaust. Paid tiers buy structured work: Plus at $11 a month ($132 annually) adds exports to RIS, CSV, BIB, PDF and DOCX plus five extraction columns at a time; Pro at $39 a month ($468 annually) adds a dedicated systematic review workflow that can screen 5,000 papers, twenty columns, and extraction across up to 135 data sources; Scale runs to $89 a month and Enterprise adds screening at 40,000 papers.

    Who it suits: anyone building a structured evidence table — a systematic or scoping review, or a methods-comparison chapter where you need the same eight fields from ninety papers. Where it falls short at doctoral scale: extraction accuracy is not verification. Every extracted cell you intend to cite must be checked against the paper, and the checking is not optional overhead — it is the review. Note also that the vendor reserves language about “PRISMA-grade” accuracy for its enterprise tier, which tells you something about how to treat the cheaper ones.

    6. Scite — citation context, if that is your problem

    Scite’s distinctive claim is that it classifies how a paper has been cited — supporting, contrasting, or merely mentioning — across “1.6B+ citations” drawn from “300M+ scholarly sources”, which lets you see whether a finding you are about to build on has actually been corroborated or quietly contradicted. Basic is $20 a month billed yearly with unlimited assistant use and collections to 1,000 papers; Pro is $50 a month with API access, larger collections and patent, clinical-trial and grant datasets. There is no free tier — only a 7-day trial — and student discounts are handled by referring your institution rather than by presenting a student card.

    Who it suits: fields with replication problems, contested effects or a retraction history worth checking. Where it falls short: classification is automated and imperfect, coverage skews to well-indexed STEM literature, and $240 a year is a real fraction of a stipend. If your field’s problem is finding literature rather than adjudicating it, this is the wrong purchase.

    What none of them do

    Four boundaries worth naming before you attribute powers to any of these tools.

    They do not read for you. A summary is a lossy compression of an argument, and the parts it loses — the caveat in the methods, the sample that is not what the abstract implies — are precisely the parts a doctorate is examined on. Anything you cite, you have opened.

    They do not confer method. A tool that screens 5,000 records does not make your review systematic. A protocol, an inclusion rule fixed in advance, dual screening where your method requires it and transparent reporting of numbers do that; the software only makes the labour survivable.

    They are not complete, and their incompleteness is uneven. Every index here is assembled from partnerships, feeds and crawls, and coverage is markedly better in indexed STEM literature than in humanities monographs, non-English scholarship, grey literature and policy documents. If your field lives in books, these tools are a supplement to your library’s catalogue, not a replacement for it.

    They are not your library, your coding software, or your writing environment. References belong in a reference manager; qualitative coding belongs in NVivo, ATLAS.ti or Taguette; the conceptual notes these tools generate belong in your notes system; and none of them is where 80,000 words gets drafted.

    A printed journal article densely annotated by hand beside a closed laptop
    The stage no tool removes. Discovery software changes how many papers reach this desk; it does not change what has to happen to them here.

    One more thing to check before you subscribe

    Two questions that cost nothing and change the answer. First, what does your university already hold? Institutional subscriptions to discovery and appraisal platforms are common and badly advertised — your subject librarian knows, and ten minutes with them is the highest-return conversation in this whole comparison. Second, what does your ethics approval and data-management plan permit? Uploading a published paper is unproblematic; uploading your own unpublished chapter, participant data or a collaborator’s manuscript to a third-party service is a different decision, governed by commitments you have already signed.

    The recommendation

    Start with a free stack and make it prove insufficient before you spend anything: Semantic Scholar for search, ResearchRabbit for citation-graph discovery, Elicit’s free tier for summarising and chatting with papers you have found. That covers the majority of doctoral literature work at zero cost.

    Then buy at most one paid tool, chosen by the specific job you cannot otherwise do. Structured extraction across dozens of papers for a systematic or scoping review — Elicit Plus or Pro. Keeping a defined literature current across a four-year candidature — Litmaps Pro at $120 a year. Adjudicating contested findings in a field with a replication problem — Scite. Buying two of these is usually a sign that the underlying problem is an unwritten inclusion rule, which no subscription fixes.

    Where the bottleneck actually moves to

    Solve discovery and the constraint relocates, quickly and predictably, to writing. Candidates who have found and read three hundred sources do not stall for want of a three-hundred-and-first; they stall converting the material into linear chapters — the failure mapped in our notes-to-chapter workflow, and the reason the introduction chapter is so often the last thing written and the worst thing written.

    Tesify is built for that stage: chapter structure, section-by-section drafting and an automatic bibliography that formats your references as you cite them. The reading, the judgement and every sentence remain yours — 100% written by you — which is the only arrangement that survives a viva. It is free to start.

    Frequently asked questions

    What is the best AI research assistant for a PhD in 2026?

    There is no single best, because discovery and extraction are different jobs. For most doctoral researchers the strongest starting configuration is free: Semantic Scholar for search, ResearchRabbit for citation-graph discovery, and Elicit’s free tier for working with papers you have already found.

    Is Elicit free?

    It has a substantial free tier — unlimited search across more than 138 million papers, unlimited summaries, chat with full-text papers and Zotero import. Paid plans start at $11 a month billed annually and exist chiefly to buy structured extraction and systematic-review screening.

    Does Scite have a free plan?

    No — only a 7-day trial. Individual plans are $20 and $50 a month billed yearly, and academic discounts run through recommending the tool to your institution rather than through a student rate at checkout.

    Connected Papers or ResearchRabbit?

    ResearchRabbit for ongoing discovery and collection-building across a candidature; Connected Papers for a fast one-off orientation to an unfamiliar area from a single seed paper. They answer different questions, and the free way to find out is to run both on a paper you know well and see which graph tells you something you did not already know.

    Can these tools do my systematic review?

    They can carry the labour — search, deduplication support, screening at volume, extraction into tables. They cannot supply the protocol, the inclusion criteria fixed in advance, the second screener your method may require, or the transparent reporting of how many records were excluded and why. The method is yours; the tool is a lever.

    Will my examiners object to AI-assisted literature searching?

    Searching and screening with software is ordinary research practice and has been since databases replaced card catalogues. What examiners test is whether you know the literature you cite — so the operative rule is that anything in your bibliography is something you have opened and read, regardless of how you found it. Follow your institution’s disclosure requirements for any generative component.

    Do these tools hallucinate references?

    The retrieval-based ones surface real indexed records rather than inventing them, which is the main argument for using them over a general chatbot for literature work. That is not a guarantee: summaries can still misstate what a paper found, and a citation you have not opened is a risk whatever produced it.

    Are they any use in the humanities?

    Less, and honestly so. Coverage is built on indexed journal literature, so monograph-based fields, non-English scholarship and archival work are poorly served. Your library catalogue, subject bibliographies and a good subject librarian remain the stronger route.

    Should I pay for one of these on a stipend?

    Only against a named job the free stack cannot do, and only after checking what your institution already licences. The comparison worth making is not the subscription against zero, but the subscription against the hours it genuinely removes from a specific piece of work.

    What about general chatbots for literature review?

    Useful for explaining an unfamiliar method or critiquing your own writing; unsuitable for finding sources, because a general model without retrieval will produce plausible references that do not exist. Fabricated citations are found in seconds at doctoral level and are catastrophic when they are.

  • Zotero vs Mendeley vs EndNote for a PhD Thesis: Which One Still Has Your References in Year Four?

    Zotero vs Mendeley vs EndNote for a PhD Thesis: Which One Still Has Your References in Year Four?

    Every guide to doctoral tooling tells you to set up a reference manager on day one, and then declines to say which. That includes our own comparison of LaTeX and Word at thesis scale, which twice insists the reference manager is the decision that matters more than the editor without ever naming one. This page settles it.

    The choice is not really about features. All three of these will insert a citation, generate a bibliography and change style on demand, and any of them handles a taught-degree essay identically well. What separates them appears only at doctoral duration and doctoral volume: three hundred sources, several gigabytes of PDFs, a supervisor who wants access, and a four-to-eight-year horizon over which your institutional affiliation is not guaranteed to be continuous.

    So the operative question is not “which has the best Word plugin”. It is which of these still has your library in year four.

    The comparison, at doctoral scale

    Zotero Mendeley EndNote
    Owner Digital Scholar (non-profit) Elsevier Clarivate
    Licence model Open source, GNU AGPL v3 Proprietary, free account Proprietary, paid version
    Free storage 300 MB 2 GB Institutional licence dependent
    Paid storage / price (2026) 2 GB $20/yr · 6 GB $60/yr · unlimited $120/yr Tiers not quoted here — see note One-time purchase, not a storage subscription
    Software cost (2026) Free Free Full €310 · Student €170 · Upgrade €140, one-time, incl. tax
    Usual doctoral route Install it yourself Install it yourself Institutional site licence
    LaTeX / BibTeX Native and well supported Supported Supported
    Risk at year four Storage cost Vendor dependence Licence expiry with your registration

    Figures were read from each vendor’s own pages in August 2026. Two verification notes, because a comparison that hides them is worth less than one that admits them. Mendeley’s paid storage tiers are not quoted here because they were not stated on the pages checked; the free allowance is advertised plainly as “Claim your 2GB of storage — it’s free” and anything beyond that is a question for Elsevier rather than a number to guess. And EndNote’s pricing is quoted in euros because that is the currency its own purchase page returned; check the figure in your own currency before you buy, and check your institution first, because you may not need to buy at all.

    The storage arithmetic nobody does in advance

    Here is the single most consequential number in this article. Zotero’s free tier is 300 MB. Paid tiers run 2 GB at $20 a year, 6 GB at $60 a year, and unlimited at $120 a year.

    Now do the doctoral arithmetic. A journal article PDF is commonly one to three megabytes; scanned material, supplementary files and book chapters are considerably larger. A candidate who reaches submission with three hundred stored PDFs is therefore looking at somewhere between roughly 300 MB and well over a gigabyte of attachments, and that is before the archival scans, the datasets and the drafts of your own chapters that people habitually park in the same library.

    The practical consequence: on Zotero, a PDF-storing doctoral library will outgrow the free tier, usually in the second year. That is not an argument against Zotero. It is an argument for knowing that Zotero costs roughly $20 to $120 a year in practice rather than nothing, and for budgeting it deliberately instead of discovering it as a stalled sync three days before a chapter deadline.

    Two ways out, both legitimate. Use Zotero’s linked-file mode, keeping the PDFs in a folder you sync yourself through whatever cloud storage you already have, so Zotero stores only the metadata and the free tier lasts indefinitely. Or pay the $20 tier and stop thinking about it. What does not work is storing everything on the free tier and hoping.

    Mendeley’s 2 GB free allowance is the most generous free storage of the three and will carry a great many doctoral libraries to submission without payment. That is a real and underrated advantage, and it is the strongest argument in Mendeley’s favour.

    A sync that has stopped because the reference library has outgrown its free storage allowance
    The failure is never dramatic. The sync just stops, usually in year two, usually the week something is due.

    The licence horizon: the EndNote question

    EndNote is different in kind from the other two. It is a one-time software purchase rather than a free account with paid storage: Clarivate’s own purchase page lists a full licence at €310, a student licence at €170 with verification, and an upgrade from EndNote 21 or earlier at €140, all one-time and inclusive of tax, with a 30-day free trial.

    But almost no UK doctoral researcher pays that, because EndNote is very commonly provided through an institutional site licence. Which is exactly where the risk lives, and it is a risk specific to doctoral study rather than to taught degrees.

    A taught student’s relationship with their institution ends cleanly at graduation, by which point the dissertation is submitted and the library is disposable. A doctoral researcher’s does not. You may spend a continuation or writing-up period in an altered enrolment status — the University of York, for instance, states that candidates in the continuation period are “not formally enrolled at the University”, a position set out with the fee implications in our guide to how long a UK PhD actually takes. You may be rebuilding a thesis for resubmission across a year in which your access has changed. You will almost certainly still be citing the same library while publishing papers from the thesis after the award.

    So the question to put to your library or IT service, in writing, before you commit four years of references to EndNote is a narrow one: does our EndNote site licence cover me during continuation or writing-up status, and for how long after my award? The answer is institution-specific, we are not going to guess it for you, and it is a two-minute email that can save you a €310 decision made under duress. Ask the same question about any other licensed software your thesis depends on while you are at it.

    The corresponding advantage of Zotero is structural rather than a feature: it is released under the GNU Affero General Public Licence version 3 by a non-profit, so nothing about your access is contingent on your continuing registration at any institution. For a project measured in years rather than terms, that is worth more than most of the feature differences.

    Group libraries, and who actually pays for them

    Shared libraries are genuinely useful in doctoral work — a group library holding the corpus for a systematic review, or one shared with a supervisor so they can see what you are reading, is a real improvement on emailing PDFs. There is a detail here that surprises people, and Zotero states it plainly: “Group file storage always draws from the storage account of the group owner.”

    Read the consequence. If you create the group, every file every collaborator adds is charged against your quota, and a 300 MB free tier will evaporate. If your supervisor creates it, it is charged against theirs. This is not a trick; it is simply a fact worth knowing before you set up a shared corpus and then wonder why your personal library stopped syncing. Where a group library is central to your project, agree who owns it and who is paying for the storage as part of the ordinary business of managing your supervisory team, rather than discovering it later.

    The migration you should do now or never

    All three export to RIS, and all three read it. Migration is therefore possible at any point, and it is also the single most reliably regretted job in doctoral tooling, because what survives an export is not what you care about.

    Bibliographic metadata migrates cleanly. What migrates badly, or not at all, is the accumulated layer that actually represents your reading: PDF annotations and highlights, notes attached to items, tags and colour coding, folder or collection structure, and the “date added” ordering that lets you reconstruct what you were thinking in your second year. Those are stored differently by each application, and an RIS file is not designed to carry them.

    Two rules follow. Choose in your first term and then stop revisiting the question — the same displacement-activity pattern that makes candidates re-litigate LaTeX versus Word every few months applies here, and it is most tempting precisely when the writing is going badly. And if you are going to move, move early, while the library is fifty items rather than four hundred and while the annotation layer you would lose is thin. Migrating a reference library during your writing-up year is a way to introduce citation errors under time pressure for no scholarly gain.

    Handing back a university card, the moment an institutional software licence ends
    Institutional software is borrowed against your registration. A doctorate is one of the few degrees where the registration can end before the work does.

    The recommendation

    Default to Zotero, and budget $20 a year for storage from the start. It is free software from a non-profit under an open licence, its data is yours in an open format, its BibTeX support is the best of the three if you are in a LaTeX discipline, and nothing about it lapses when your registration does. The 300 MB free tier is its one genuine weakness at doctoral scale and it costs twenty dollars to solve.

    Choose Mendeley if the free storage is decisive for you and you are comfortable with an Elsevier account holding your library. Two gigabytes free is the most generous allowance here and it may well carry you to submission at no cost.

    Choose EndNote if your department’s workflow is built on it — shared team libraries, an established local template, a supervisor who will exchange libraries with you — and you have confirmed in writing how long your institutional licence covers you. Local convention is a real and legitimate reason, and swimming against it costs more than it looks like it costs.

    What matters far more than which of the three you pick is that you pick one in your first term and enter every source as you read it. The most expensive tooling mistake available to a doctoral researcher is not choosing the wrong manager; it is arriving at 250 sources with no bibliographic database at all and rebuilding it by hand in the month you can least afford.

    What a reference manager will not do

    It stores and formats what you have already found and decided to keep. It does not find the literature — that is the job of the discovery and appraisal tools compared in our roundup of AI research assistants at doctoral scale. It does not code your qualitative data, which belongs in NVivo, ATLAS.ti or Taguette. It does not hold your thinking, which belongs in your notes system. And it does not tell you whether a source you have stored is one you have actually read, which remains the only standard your examiners will apply to your bibliography.

    It also does not write the chapters. When the library is in order and the drafting is what has stalled, Tesify holds the chapter structure and formats your bibliography as you cite, so the references land correctly in a document that stays internally consistent across 80,000 words. Every sentence stays 100% written by you, which is the only arrangement that survives a viva. It is free to start.

    Frequently asked questions

    Which reference manager is best for a PhD?

    Zotero for most candidates: free, open source under the AGPL v3, strong BibTeX support, and not tied to your institutional registration. Budget for storage, because the free tier is 300 MB and a PDF-storing doctoral library will exceed that.

    Is Zotero really free?

    The software is free and open source. Cloud storage is not beyond 300 MB: 2 GB is $20 a year, 6 GB is $60, and unlimited is $120. You can avoid the cost entirely by storing PDFs as linked files in your own cloud folder and letting Zotero sync only the metadata.

    How much storage does a doctoral reference library need?

    More than 300 MB if you store PDFs. At one to three megabytes per article, three hundred stored papers alone can approach or exceed a gigabyte, before scans, datasets or your own drafts. Plan for a gigabyte or two rather than hoping.

    How much free storage does Mendeley give you?

    Two gigabytes, advertised on Elsevier’s own page as “Claim your 2GB of storage — it’s free”. That is the most generous free allowance of the three and is enough for a great many doctoral libraries.

    How much does EndNote cost?

    Clarivate lists a full licence at €310, a student licence at €170 with verification, and an upgrade from EndNote 21 or earlier at €140 — all one-time purchases including tax, with a 30-day trial. Check your institution before buying, since site licences are common.

    Will I lose EndNote when I finish my PhD?

    If you are using an institutional site licence, possibly, and the timing is the problem: doctoral researchers often need the library during continuation status, during a resubmission period, and while publishing from the thesis after the award. Ask your library in writing what your licence covers and for how long, rather than assuming either way.

    Can I move my library from one manager to another?

    Yes, via RIS export, which all three support. The metadata transfers reliably; annotations, notes, tags and collection structure often do not. Move early if you are going to move, and never during your writing-up year.

    Whose storage is used by a shared group library?

    In Zotero, the owner’s: “Group file storage always draws from the storage account of the group owner.” Decide who creates the group before you build a shared corpus, because that decision determines whose quota pays for it.

    Which works best with LaTeX?

    Zotero, whose BibTeX export is well supported and widely used with Overleaf workflows. All three can produce BibTeX, but if you are writing in LaTeX this is a real point of difference rather than a marginal one.

    Does it matter which one my supervisor uses?

    Only if you intend to share a library or exchange files directly, in which case matching them removes real friction. For ordinary supervision it does not matter at all, because what they receive is a document with formatted citations, not your database.

    Should I switch reference managers during corrections?

    No. Corrections run against a deadline and re-generating a bibliography in a new application is a reliable way to introduce citation errors at exactly the wrong moment. Make the corrections where the thesis already lives.

    Do I still need a reference manager if I only have 80 sources?

    Yes, and the reason is not the count but the changes. A doctoral bibliography gets reordered, restyled and partially rewritten repeatedly across the final year, and every one of those operations is instant in a manager and a manual reformatting job without one.

  • UKRI Studentship vs Self-Funded PhD: What the Decision Actually Costs (2026)

    UKRI Studentship vs Self-Funded PhD: What the Decision Actually Costs (2026)

    This decision is usually framed as “can I get funding, and if not, should I pay?”. That framing hides the two things that actually differ, which are how much control you have over the research question and how much of your own money and time is at risk. Comparison first, then the numbers behind it.

    UKRI-funded studentship Self-funded, home student Self-funded, international student
    Fees Paid from the training grant Pegged to the UKRI rate — £5,238/yr at Queen Mary for 2026-27 Set by the institution — £25,350 or £30,950/yr at York for 2026/27
    Stipend Yes, tax-free, at or above the UKRI minimum None None
    Who chose the project Often the supervisor or centre, from an advertised list Usually you Usually you
    Funded period Defined in your offer letter; no sector-wide figure exists Undefined — bounded only by registration limits Undefined, and visa-bounded
    Training and RTSG Normally included, with a research training support grant Negotiable, often charged as a fee band Negotiable, often charged as a fee band
    Cohort Usually, especially in a DTP or centre Depends entirely on the department Depends entirely on the department
    Realistic mode Full-time Frequently part-time alongside work Full-time, because the visa requires it

    The fee asymmetry that decides most of this

    UK home doctoral fees are effectively capped by the sector’s own convention of following the research council rate. Queen Mary University of London charges home research students £5,006 full-time in 2025-26, rising to £5,238 for 2026-27, with part-time at exactly half, and explains why: “tuition fees for Home students are set by UK Research Council (UKRI) and each year UKRI usually increases their fee and stipend levels in line with inflation.” The University of York states the same principle as policy — for UK home students, “fees will increase in line with, and no higher than, the prevailing UKRI fee rate as published at ukri.org”.

    International fees are not pegged to anything of the kind. York’s international research fees for 2026/27 sit at £30,950 a year for its science and technical band and £25,350 for humanities and social sciences, with part-time typically half, and increases running “in line with inflation (determined by the Consumer Price Index inflation rate, up to a maximum of 10%)”.

    Put three or four years against those numbers and the two self-funded routes are not the same decision at all. A home candidate at the pegged rate is looking at roughly £16,000 in fees over three years. An international candidate in York’s science band is looking at roughly £93,000 in fees over three years — before rent, before a single conference, and before the years many candidates need beyond the third.

    Neither figure includes what departments charge on top. Queen Mary notes that some programmes carry additional “fee bands” covering “equipment, laboratory consumables, specialist technical support, data processing, training and travel”, and York flags separate research training support charges for laboratory consumables, fieldwork, travel and conference expenses. On a funded studentship these are usually met by the research training support grant. Self-funded, they are yours, and they are the costs applicants forget entirely.

    A candidate reading advertised doctoral studentships rather than proposing their own project
    The funded route usually means choosing from projects someone else defined. That is the real trade, and it is not obviously a bad one.

    Route 1: The UKRI-funded studentship

    Fees paid from a training grant, a tax-free stipend at or above the published minimum, a research training support grant, and a set of entitlements — leave, mode changes, phased return — that come with published terms behind them. The figures and the terms are set out in our roundup of UK PhD stipends and studentship funding, and we will not restate them here.

    Who it suits: anyone whose research interests can be made to fit an advertised project, or who can win an open competition with a proposal of their own. Applications for the latter live or die on the proposal, which our guide to the PhD research proposal for a UK studentship covers in detail.

    Where it falls short. The project may not be yours. Studentships attached to a supervisor’s grant or a centre’s theme come with the question substantially defined, and “close enough to my interests” in year one can feel very different in year three. The funded period is finite and enforced in practice by the expectation that you submit within it. And UKRI does not publish a sector-wide duration — the length is devolved to the awarding council, so your offer letter is the only authority on how long the money lasts.

    One thing that is not a shortcoming, contrary to a persistent belief: nobody can tell you your odds. UKRI states plainly that it does not publish data on application and award rates for studentship starts, so any quoted success rate for a research council studentship is somebody’s guess.

    Route 2: Self-funding as a home student

    At the pegged rate this is the least-discussed viable route in UK doctoral education. Fees of a few thousand a year are within reach of many people working full-time, and part-time registration turns the doctorate into a long project rather than a career interruption.

    Who it suits: someone with a question of their own that no advertised studentship covers; someone whose employer will contribute or give study time; someone mid-career who cannot live on a stipend and does not need to; and anyone for whom the alternative is not doing the doctorate at all.

    Where it falls short at doctoral scale. The obvious cost is that you earn nothing extra and pay fees for four to seven years, since part-time routes run long. The less obvious costs are the ones that actually bite. You are not in a cohort by default, and isolation is the sector’s most consistently reported doctoral problem — the figures in our summary of PhD wellbeing and supervision satisfaction put financial difficulties behind mental and emotional health among reasons researchers consider leaving, and both are worse without a peer group. Training that a DTP would supply and fund may be chargeable. And there is no external deadline: nothing stops a self-funded part-time doctorate drifting except your own structure, which is why the registration limits in your regulations matter more to you than to a funded candidate.

    What self-funding does not buy you is a lower standard. Examiners apply the same doctoral criteria whatever paid for the fees, and the requirement for an original contribution is identical.

    Route 3: Self-funding as an international student

    The same route with an order of magnitude more money at stake and materially less flexibility. Three constraints stack on top of everything in Route 2.

    Fees are the institution’s own and can approach thirty-one thousand pounds a year, as York’s science band shows, with annual increases tracking CPI up to a cap of ten per cent — so budget on the fee rising each year rather than holding. Visa conditions typically require full-time study, which removes the part-time option that makes home self-funding workable, and they restrict how much you can work. And UKRI contributes only up to home fee rates where a studentship is involved at all, so even a partial award may leave a fee gap to cover.

    The honest recommendation for this group is to widen the search before accepting a self-funded offer: overseas government scholarships, university fee waivers and fee-band awards, charity and trust funding, and departments that price research degrees at or near the home rate for particular schemes. A funded place at a less prestigious department is almost always a better outcome than a self-funded place at a famous one.

    A self-funded part-time doctoral researcher working in the evening after a day at work
    Self-funding part-time is a real route, and it is bounded by your own structure rather than by anyone else’s deadline.

    The seven questions to ask before accepting either

    1. What exactly is funded, and for how long? Fees, stipend, RTSG, conference budget — itemised, in writing, with the funded period stated. There is no national number to fall back on.
    2. Whose project is it? If the question is already defined, read it as the thing you will live inside for four years, not as a starting point you will negotiate later.
    3. What happens if you overrun? Ask what registration status and what fee apply after the funded or expected period ends. This is the single most under-asked question in UK doctoral admissions.
    4. What is charged on top? Get the fee bands and research training support charges in writing before you accept, self-funded or not.
    5. Is there a cohort? Not a marketing claim — ask how many research students the group currently has and whether they meet.
    6. What is the supervisory arrangement? Self-funded candidates sometimes assume they will be supervised more lightly. They should not be, and our guide to managing your supervisory team sets out what the arrangement should look like either way.
    7. Would a professional doctorate serve you better? If your question comes from your own practice and your employer might contribute, the taught-plus-research architecture may fit better than a self-funded PhD — the comparison is in our page on professional doctorates versus the PhD.

    The recommendation

    Take the funded studentship if one is available and the project is within reach of what you actually want to research. The stipend, the training grant, the cohort and the entitlements are worth more than the autonomy you give up, and the autonomy is usually less constrained in practice than it looks on the advert.

    Self-fund as a home student only with a question you genuinely own, a realistic view of a part-time timeline, and a written answer to the overrun question. At the pegged fee rate this is a defensible decision, and it is the right one for a lot of mid-career candidates who will never fit a studentship advert.

    Think very hard before self-funding as an international student at full fees. Ninety thousand pounds of fees for a qualification whose academic career payoff is uncertain is a serious financial decision, not an academic one — the destination data for UK doctoral graduates is worth reading before committing, and so is the current scale of doctoral study in the UK, which we set out in our page on how many PhD students there are.

    Whichever route you take, the variable that most reliably determines whether you finish is not funding — it is whether writing keeps moving from year one. Tesify holds the chapter structure and the bibliography across the whole candidature, which matters most on the long part-time routes self-funding tends to produce. Everything stays 100% written by you, and it is free to start.

    Frequently asked questions

    How much does a self-funded PhD cost in the UK?

    For a home student, fees are effectively pegged to the UKRI rate — Queen Mary charges £5,238 full-time for 2026-27, with part-time at half. For an international student the fee is the institution’s own: York charges £30,950 a year in its science band and £25,350 in humanities and social sciences for 2026/27. Neither figure includes living costs or research training charges.

    Why are home PhD fees so much lower than international ones?

    Because the sector follows the research council rate for home students. York states that home fees “will increase in line with, and no higher than, the prevailing UKRI fee rate”, while international fees rise with CPI up to a ten per cent cap. There is no equivalent peg on the international side.

    Is a self-funded PhD worth less than a funded one?

    No. Examiners apply the same doctoral criteria and the award is the same. The differences are financial and structural — cohort, training, deadline pressure — not academic.

    Can I do a self-funded PhD part-time while working?

    As a home student, commonly yes, and part-time fees are typically half the full-time rate. As an international student on a study visa this is usually not available, because the visa conditions require full-time study.

    What are fee bands and bench fees?

    Additional charges some programmes levy for equipment, consumables, technical support, data processing, training and travel. Queen Mary describes them explicitly, and York flags separate research training support charges. Ask for them in writing before accepting an offer.

    What are my chances of getting a UKRI studentship?

    Unknowable from published data. UKRI does not publish application and award rates for studentship starts, so any success-rate figure you see quoted is an estimate rather than a statistic.

    How long does a UKRI studentship fund you for?

    There is no sector-wide answer. UKRI devolves the duration to the awarding research council and the research organisation, so your advert and offer letter are the only authoritative sources.

    Can I switch from self-funded to funded partway through?

    Occasionally, through internal competitions, departmental awards or a supervisor’s grant. It is not a plan, and no admissions office will promise it. Treat any such possibility as upside rather than as part of your budget.

    Do self-funded students get the same supervision?

    They should. Supervision is an institutional obligation attached to registration, not to the source of the fee. If the arrangement in practice feels lighter, that is a matter to raise formally rather than to accept.

    Is it cheaper to do a PhD part-time?

    Per year, yes — part-time fees are typically half. In total, usually not, because the route runs longer and you pay for more years. What part-time buys is an income while you study, not a discount.

    What happens to fees if I overrun?

    That depends on your institution’s registration statuses and their charges, and it is the question applicants most often fail to ask. Get the answer in writing before accepting, because an unfunded overrun year is a common and under-discussed part of the UK doctorate.

  • Best Transcription Tools for PhD Research Interviews (2026): Accuracy, Ethics and Cost Compared

    Best Transcription Tools for PhD Research Interviews (2026): Accuracy, Ethics and Cost Compared

    Transcription sits at an awkward junction: it is the most automatable drudgery in qualitative research, and it involves shipping your participants’ voices — personal data by definition — to whatever service you picked at midnight. So this comparison runs compliance first, capability second: the fastest transcriber in the world is the wrong choice if your ethics approval and data-management plan never named it.

    Local AI (Whisper-class) Otter.ai Your university’s Microsoft 365 tenant Human transcription services Doing it yourself
    Where audio goes Nowhere — processed on your machine Otter’s cloud Institutional cloud, inside existing agreements The service’s staff and systems Nowhere
    Cost Free (open-source) Free tier 300 min/month; Pro $8.33/month annual (1,200 min); 20% student discount via .edu email Usually included Per audio hour; the premium option Your time: ~4–8 hours per audio hour
    Accuracy on clear audio Strong Strong Good Best, especially accents and crosstalk Perfect, eventually
    Accuracy on messy audio (cafés, dialect, jargon) Degrades; better with larger models Degrades Degrades Holds up best Holds up, slowly
    Ethics-form friendliness Highest — no third-party processing Requires naming a third-party processor High — often pre-approved infrastructure Needs confidentiality agreement Highest
    Best for Most doctoral interview studies Meetings and low-sensitivity recordings Institutionally cautious projects Difficult audio, funded projects Small N, analytic immersion

    Start with the rule, not the tool

    Interview recordings are personal data — a voice is identifiable even before the content is — and your handling of them is governed by what your ethics application, participant information sheet and data-management plan actually said. Universities increasingly publish lists of approved transcription routes or require a data-protection assessment before audio leaves institutional systems, and sending recordings to an unapproved consumer cloud service can put you in breach of commitments you signed, whatever the tool’s own privacy page says. The sequence that keeps you safe: name your transcription route in the ethics application; describe it in the information sheet (“recordings will be transcribed using…”); and if your plans change mid-project, amend the approval rather than improvising. If you are before that stage now, write the transcription paragraph today — it is ten minutes that removes the whole class of problem, and the same logic applies to every tool that touches participant data.

    The shortlist, ranked for a doctorate

    1. Local AI transcription — the new default

    Open-source speech models of the Whisper family changed this decision: strong automatic transcription that runs on your own computer, so the audio never leaves your possession. That single property collapses most of the compliance analysis — there is no third-party processor to name, justify or trust — and the price is zero. Costs: you need a reasonably capable machine, a small amount of setup (your university’s research computing team or a colleague has almost certainly done it already), and accuracy still degrades on poor recordings. For a typical doctoral interview study — sensitive-ish data, dozens of hours, no transcription budget — this is the recommendation.

    2. Your university’s Microsoft 365 transcription — the institutional route

    If your university runs Microsoft 365, transcription inside Word or Teams processes audio within the institutional tenant — infrastructure your university has already contracted for, which is why data-protection teams often point students here first. Accuracy is serviceable rather than stellar, and speaker separation is basic, but “the recording never left university systems” is a sentence that makes ethics reviewers relax. Check your institution’s own guidance for what its licence covers.

    3. Otter.ai — capable, for the right recordings

    Otter is polished and fast, with live transcription, speaker labelling and a workable free tier (300 minutes a month; Pro at $8.33 a month billed annually raises it to 1,200 minutes, with a 20 per cent student discount via a .edu address). The catch is precisely its cloud nature: for research interviews it is a third-party processor of participants’ personal data, which your approval must cover. Where it fits: low-sensitivity recordings, your own research memos, supervision meetings — and interview studies whose approval explicitly names it.

    4. Human transcription services — buy them for the hard cases

    Professional transcribers still beat every machine on strong accents, overlapping speech, poor recordings and specialist vocabulary, and offer the choice of intelligent verbatim versus full verbatim done judgementally rather than mechanically. They cost real money per audio hour and add a confidentiality step (a signed agreement, a reputable service, an approval that mentions outsourcing). Rational uses: a funded project, a handful of unusable-by-machine recordings, or Deaf/accessibility workflows.

    Audio recorder and consent forms after a research interview
    The consent form and the transcription route are one decision: participants agreed to a specific handling of their voices.

    Recording well is half the transcription problem

    Every tool in the table performs a tier better on good audio, and good audio is mostly free. Use a dedicated recorder or a decent phone app rather than a laptop microphone across the table; put the device nearer the participant than yourself, since their words matter more than your questions; choose the quiet room over the atmospheric café whenever the participant allows; record thirty test seconds and listen back before the interview proper; and for remote interviews, use the platform’s own recording rather than re-recording speaker audio through the air. One more habit that saves projects rather than minutes: duplicate the file to institutional storage before you leave the building or close the call. A machine transcript of a clean recording plus a light correction pass beats a human transcript of a bad one — and the recording is the only stage you can never redo.

    The step everyone skips: correction is analysis

    Whatever produces your first draft transcript, the checking pass — you, headphones, audio against text — is not optional overhead. Automatic transcripts fail exactly where interviews are most interesting: jargon, names, emotional speech, overlaps. And the correction hours are quietly the first analysis pass; researchers routinely report their best early codes emerging while fixing transcripts. Budget roughly one to two hours per audio hour for correction of machine output — against four to eight for typing from scratch — and log your conventions (how you marked pauses, laughter, redactions) because your methods chapter will need them. From there the transcripts flow into coding — NVivo, ATLAS.ti or Taguette — while the conceptual notes they generate belong in your notes system, not a folder of stray documents.

    The recommendation

    Run local Whisper-class transcription as your default; use your university’s Microsoft 365 transcription where institutional processing is the path of least resistance; pay humans for the recordings machines cannot handle; and use consumer cloud tools only where your ethics approval names them. In every case, the tool appears in your ethics paperwork before it appears in your workflow.

    And when the transcripts are coded and the findings chapter looms, the bottleneck moves back to the writing itself. Tesify structures and drafts the thesis with you — chapters, methods documentation and bibliography in one workspace, 100% written by you — so the months you saved on transcription arrive intact at the examination.

    Frequently asked questions

    What is the best free transcription tool for research interviews?

    Local Whisper-class transcription: free, strong on clear audio, and — because it runs on your own machine — the easiest route to justify in an ethics application. Your university’s included Microsoft 365 transcription is the runner-up.

    Can I use Otter.ai for PhD interviews?

    Only if your ethics approval and participant information cover a third-party cloud processor — name it, justify it, and check whether your university restricts it. For meetings and non-participant audio it needs no such ceremony.

    Is it a GDPR problem to upload interviews to a transcription site?

    It is a data-processing decision that must match what participants consented to and what your university permits. Voices are personal data; an unapproved upload can breach your own signed commitments even where the service itself is reputable.

    How long does transcription actually take?

    Typing from scratch: commonly four to eight hours per audio hour. Machine-first with human correction: the machine minutes plus one to two hours of checking per audio hour. The correction time is real work — and doubles as first-pass analysis.

    Do I have to transcribe every interview in full?

    Methodologically, it depends on your analysis: some approaches require full verbatim transcripts; others defensibly work from full transcripts of core interviews plus indexed partial transcripts elsewhere. Whatever you choose, state and justify it in the methods chapter.

    Should I use intelligent verbatim or full verbatim?

    Full verbatim (every um, repair and overlap) where the interaction itself is analysed, as in conversation-analytic work; intelligent verbatim (cleaned for readability, meaning preserved) for most thematic work. The decision belongs to your method, not your transcriber.

    How should I store recordings and transcripts?

    Exactly as your data-management plan says: institutional storage, not personal devices; recordings and identity keys separated from transcripts; pseudonymisation applied at transcription time; deletion on the schedule you promised participants.

    Can AI transcription handle strong accents and dialects?

    Less well than clear standard speech — error rates rise, and rise most on precisely the participants whose voices are least represented in training data. Pilot your tool on your hardest expected audio before committing, and budget human help for what fails.

    Do I need participants’ consent to use AI transcription?

    You need consent that covers your actual processing. The clean practice is to describe the transcription route in the participant information sheet; a vague “recordings will be transcribed” plus a later cloud upload is where problems start.

    What should the methods chapter say about transcription?

    The route (tool or service, and where processing happened), the verbatim convention, the correction process, anonymisation practice, and the storage arrangements — three or four sentences that jointly demonstrate the data was handled as approved.

  • Obsidian vs Notion for PhD Thesis Notes: Which Survives Four Years? (2026)

    Obsidian vs Notion for PhD Thesis Notes: Which Survives Four Years? (2026)

    Choosing a notes system for a doctorate is not like choosing one for a job you might leave next year. Whatever you pick will hold several thousand notes by submission, must still open instantly in year four, and must give everything back cleanly when the thesis is done. Judge both tools against that horizon, not the onboarding demo. Comparison first, verdict after.

    Obsidian Notion
    Data model Plain Markdown files on your own disk Cloud workspace of pages and databases
    Price App free, including commercial use; Sync $4/user/month (annual) is optional — free sync workarounds exist Free plan; Plus €9.50/member/month — and the Plus plan (1-member) is free for students via a school email
    Offline access Total — it is local files Limited; built cloud-first
    Linking and graph Core strength: wiki-links, backlinks, graph view Links exist; databases with properties are the real organising tool
    Structured data (reading logs, participant trackers) Via plugins and properties; less natural Core strength: databases, views, filters
    Longevity of your notes Markdown readable by any future tool, no export step needed Export exists (Markdown/CSV) but structure degrades on the way out
    Extensibility Large community plugin ecosystem Integrations and API; AI features on paid tiers
    Failure mode at scale Plugin sprawl and vault clutter Sluggish giant databases; structure becomes its own project

    The two tools disagree about what a note is

    Obsidian treats notes as documents you own: a folder of Markdown files on your machine, linked into a network. Nothing leaves your disk unless you choose sync; the app is free including commercial use, with optional paid services (Sync at $4 a month billed annually, Publish, a voluntary $50/year commercial licence) funding development. For the Zettelkasten-style working pattern — hundreds of small linked notes whose connections surface as arguments — it is the natural home, and the graph of backlinks becomes a map of your literature. The deeper doctoral argument for it is brutal and simple: in year four, your notes are text files. Any tool, any decade, can read them. The export problem cannot happen because there is nothing to export.

    Notion treats notes as records in databases: pages with properties, filtered into views. For the structured side of a doctorate it is genuinely better — a reading log with status, method and relevance fields; a participant tracker; a supervision-meeting database that generates your agenda. Students get the Plus plan free (single-member) with a school email, which removes price from the argument. The costs are the mirror image: it is cloud-first, so patchy connectivity on fieldwork is a real constraint; big databases slow down; and while export to Markdown and CSV exists, the relational structure you spent years building does not survive the trip out cleanly. Notion’s AI features sit on limited trials outside the paid tiers — pleasant, but nothing a doctorate depends on.

    Index cards linked with thread mapping thesis ideas
    The linking pattern is the Obsidian case; the tracking pattern is the Notion case. A doctorate contains both.

    The three-year test

    Before committing, ask each candidate tool the questions that only matter later. Can you find every note touching one concept in under a minute, once there are three thousand of them? Backlinks and saved database views both pass; folder hierarchies alone do not. Does it work on the train, in the archive basement, at the fieldwork site — or does it quietly assume connectivity? What is the realistic total cost across four years, including the sync or plan you will actually need rather than the tier on the comparison page? If the company changed its pricing or shut down in year three, what exactly would you lose, and how fast could you leave? And — least asked, most predictive — does its working grain match yours, because a tool you resent opening is a tool you will stop opening by Easter of year one. Obsidian and Notion give opposite answers to several of these; that, not feature counts, is the real comparison.

    The recommendation

    For the thinking layer of a PhD — literature notes, concept development, argument building — use Obsidian. Local files, free without conditions, and structurally incapable of trapping four years of notes. Use Notion where its databases genuinely earn their keep — project tracking, reading logs, supervision admin — on the free student Plus plan. Many researchers run exactly this split happily; if you refuse to run two tools, pick the one matching your dominant work pattern: linking and prose → Obsidian; tracking and structure → Notion.

    Two honest caveats. Obsidian’s flexibility is a procrastination hazard — plugin-tinkering and vault-gardening feel like work and are not; adopt a minimal setup and freeze it for a term. And Notion’s databases invite the same failure at a different altitude: a perfect tracking system for reading you are not doing. The tool is the container; the doctorate is the reading and writing.

    What neither tool does

    Three gaps worth naming before you commit either tool to jobs it cannot do. Neither is a reference manager — Zotero (or your lab’s equivalent) still owns citations and PDFs, and both notes tools link out to it well enough. Neither is a QDA package — if your method needs systematic coding of transcripts, that is NVivo/ATLAS.ti territory, not a notes app. And neither is where the thesis gets written: at 80,000 words with hundreds of references, drafting lives in a proper long-form environment — the trade-offs are in our LaTeX vs Word comparison — or in a workspace built for thesis drafting.

    That last hand-off is the one that matters most and gets planned least. Notes become a thesis through a deliberate pipeline: distilled claims move from the vault into a chapter outline, sources attach to claims, and drafting proceeds against that outline rather than against three thousand raw notes. Tesify is built for that stage — chapter structure, drafting and an automatic bibliography in one place, 100% written by you — and the full notes-to-chapter method is in our writing-up workflow guide.

    Setting up either tool for a doctorate in one evening

    For Obsidian: one vault for the whole doctorate; folders kept to a handful (literature, concepts, methods, admin); one note per source and one per concept; links made at write-time, not in gardening sessions; the vault folder inside your backed-up university storage; and no more than five plugins until the habit is stable. For Notion: one workspace; a source database with the fields you will actually filter by (status, method, chapter relevance); a supervision database whose entries double as agendas; and a weekly view you visit on a schedule. In both: write the first fifty literature notes before customising anything further — real content teaches you what structure you need better than any template gallery.

    Frequently asked questions

    Is Obsidian really free for a funded PhD researcher?

    Yes — the app is free without limits for personal and commercial use alike; the commercial licence is optional support, and the paid products (Sync, Publish) are add-on services, not unlocks. Syncing through your university cloud storage instead of paid Sync is common and free.

    Is Notion free for PhD students?

    Notion’s education offer gives students the Plus plan free as a single-member workspace via an eligible school email — and the eligibility list extends well beyond .edu domains. AI features remain limited-trial outside paid tiers.

    Which is better for a Zettelkasten?

    Obsidian, without much contest — atomic linked notes, backlinks and the local graph are its native model. Notion can imitate the pattern with relations, but you are working against its grain.

    Can I use both without the setup eating my week?

    Yes, and the stable split is by function: Obsidian for thinking (literature and concept notes), Notion for tracking (reading log, supervision admin, project boards). The one rule that keeps it sane: each kind of content lives in exactly one place.

    What happens to my notes when the PhD ends?

    Obsidian: nothing — they are Markdown files you already possess. Notion: export the workspace (Markdown/CSV) and expect the database structure to flatten. Whichever you choose, rehearse the exit once in year one so the end of the doctorate holds no surprises.

    Which handles PDFs and annotations better?

    Neither should be your PDF library — that is the reference manager’s job. Keep PDFs and highlights in Zotero and write the intellectual note (claim, evidence, relevance to your argument) in your notes tool, linked by citekey.

    Is Obsidian’s graph view actually useful or just pretty?

    Mostly pretty at whole-vault scale; genuinely useful filtered to a cluster — the local graph around one concept surfaces connections and orphaned notes you had forgotten. Treat it as an occasional audit tool, not a workspace.

    How do these tools handle collaboration with supervisors?

    Notion is far better for shared artefacts — a meeting database or progress board your supervisors can see. Obsidian is fundamentally single-player; researchers share exports, not vaults. Most supervision workflows need only a shared document anyway.

    What about Roam, Logseq and the other networked-notes tools?

    The criteria transfer directly: who holds the files, what exports, what it costs across four years, and which working pattern it serves. Obsidian and Notion sit at the two poles — local-linked versus cloud-structured — which is exactly why this comparison generalises.

    Do I need any of this to finish a PhD?

    No — doctorates were finished with index cards, and some still are. A notes system pays off in retrieval: the moment in year three when you need every note touching one concept, in seconds. If your current system already does that, keep it and spend the energy on writing.

  • NVivo vs ATLAS.ti vs Taguette for Qualitative PhD Research in 2026 (They Are Not All Rivals Any More)

    NVivo vs ATLAS.ti vs Taguette for Qualitative PhD Research in 2026 (They Are Not All Rivals Any More)

    Start with a fact that reframes most of the comparisons you will find online: NVivo and ATLAS.ti are now products of the same company. Lumivero acquired QSR International, NVivo’s publisher, with the legal entity consolidation taking effect on 1 July 2023, and announced its acquisition of ATLAS.ti on 12 September 2024. Lumivero lists both under its research and qualitative data analysis portfolio.

    This matters for two reasons. Older head-to-head comparisons were written when these were competing companies with an incentive to differentiate, so their framing is now dated. And a market where the two dominant tools share an owner is one where you should think harder than usual about lock-in.

    NVivo ATLAS.ti Taguette
    Owner Lumivero Lumivero Rémi Rampin and contributors
    Licence Proprietary Proprietary Open source (BSD 3-Clause)
    Cost to you Commercial; often a university site licence Commercial Free
    UK site licences Common Less common Not applicable
    Feature depth Very high Very high Deliberately minimal
    Access after your registration ends Usually lost Usually lost Retained
    Best suited to Large mixed-method projects in NVivo departments Visual, network-oriented analysis Interview coding where simplicity is a feature

    NVivo

    NVivo is the most widely licensed qualitative analysis package in UK universities, and that institutional footprint is its main practical advantage. The University of Manchester, for example, announced in June 2025 that NVivo 14 and 15 are available for all staff and students working in the UK or Ireland. Where a site licence exists, so usually do local training sessions, library guides and colleagues who can help when something breaks — support that is worth more mid-project than any feature.

    Strengths at doctoral scale. It handles large and heterogeneous datasets well: interviews, focus groups, documents, survey open-text and media in one project. Its matrix and comparison queries are genuinely useful when you want to examine how coding varies across participant attributes, which is exactly the kind of analysis a doctoral project reaches in its second year and an undergraduate project never does.

    Weaknesses. The interface rewards investment; expect to lose real time early. Licensing is the sharper issue — student licences are typically time-limited, and access normally ends with your registration. Lumivero states that it supports two previous versions, which matters if you return to a project after a gap.

    ATLAS.ti

    ATLAS.ti has a long lineage — the company behind it was founded in 1993 — and a distinct analytical character. Where NVivo’s mental model is a hierarchy of codes, ATLAS.ti’s is a network: it is built around linking quotations, codes and memos and then visualising those relationships.

    Strengths. If your analysis is genuinely relational — grounded theory work where you are building connections between concepts rather than counting occurrences within them — the network view is not decoration; it is the analysis. Its quotation-level linking is more natural than NVivo’s for this style of work.

    Weaknesses. UK institutional licences are less common than for NVivo, so you are more likely to be paying yourself or working within a departmental allocation. Its published pricing page was unavailable at the time of writing, so confirm current student licensing directly with the vendor rather than relying on figures quoted in older guides.

    The ownership point again. Two products under one owner may converge, and the differentiation between them may narrow over time. Nothing about that is sinister, but it argues against choosing on the basis of a feature gap that may not persist for the length of your candidature.

    Colour-coded thematic codes applied across qualitative data
    Software organises coding; it does not perform it. The interpretive decisions remain entirely yours.

    Taguette

    Taguette is free and open source, released under the BSD 3-Clause licence and maintained by Rémi Rampin with contributors. It is actively maintained — its repository showed commits in 2026 at the time of writing — and a hosted version is available alongside a local install.

    One caution when assessing it: its GitHub mirror lists releases only up to 2019, because releases are cut on GitLab rather than the mirror. Read that as a packaging artefact, not as abandonment, which is a mistake some comparison articles make.

    Strengths. It does the core job — importing documents, highlighting, tagging, exporting coded extracts — and it does not expire. Your project remains yours after submission, after your viva and after your registration ends. For interview-based doctoral work with a manageable number of transcripts, the feature gap against the commercial tools is smaller than their marketing implies.

    Weaknesses. Deliberately minimal. No sophisticated query language, no attribute-based matrix analysis, no substantial visualisation. If your project needs to interrogate coding patterns across participant characteristics, you will hit the ceiling. Institutional support is unlikely to exist, so you are your own help desk.

    The criterion doctoral researchers under-weight

    Ask what happens to your project files when your licence ends.

    This is a doctoral problem specifically. Undergraduate projects finish and are never revisited. A doctoral project generates papers for years afterwards — and returning to your coded data to check a claim for a reviewer, eighteen months after submission and six months after your account closed, is a genuinely common scenario.

    Three mitigations, whichever tool you choose. Export your coded extracts in an open format at every major milestone rather than only at the end. Keep your codebook as a separate document, not only inside the software. And before your registration ends, export everything and check the export actually opens.

    The recommendation

    Use NVivo if your university holds a site licence and your department teaches it — local support outweighs feature differences, and it comfortably handles doctoral-scale projects.

    Use ATLAS.ti if your analysis is genuinely network-oriented and you have funded access.

    Use Taguette if your project is interview coding at a moderate scale, if you have no institutional licence, or if long-term access to your own coded data matters more than query features. It is a legitimate scholarly choice, not a compromise.

    And do not overlook the fourth option: coding by hand, in a spreadsheet or a word processor with a disciplined codebook. For a project with fifteen interviews and a reflexive thematic approach, this remains entirely defensible, and some methodologists prefer it on the grounds that it keeps you closer to the data.

    What the software will not decide for you

    No package chooses your analytical approach, and examiners assess the approach rather than the tool. Naming your software in the methods chapter is necessary; it is not a methodology. “Data were analysed using NVivo” describes a container, not a method — you need to state the analytic approach, how codes were generated, how themes were developed and who was involved.

    Nor will any tool tell you when you have enough data, or what your findings mean once coded. That interpretive work is what the discussion chapter exists to carry, and what ultimately supports the claim examined in our guide to stating an original contribution to knowledge. If you are earlier in the process, your analytical strategy is also one of the things a panel probes at the upgrade or confirmation review — and naming a package there without describing a method is a reliable way to invite a difficult question.

    If the coding is done and writing it up is the bottleneck, you can draft those chapters in Tesify from your own themes and extracts — the interpretation stays 100% written by you.

    Frequently asked questions

    Do examiners care which qualitative software I used?

    No. They care that your analytic procedure was systematic, transparent and appropriate to your methodology. A thesis that names a package but cannot describe how codes became themes is weaker than one that coded by hand and explains the process fully.

    Is it acceptable to code a PhD by hand?

    Yes, and it remains common in some methodological traditions. Describe your procedure carefully and keep an auditable codebook. Software makes management easier at volume; it does not confer rigour.

    Can I move a project between these tools?

    Partially, and expect loss. Coded extracts and codebooks usually survive export and import; memos, links and network structures often do not. Choose early and commit, rather than planning to migrate mid-analysis.

    Does using software mean my analysis is quantitative?

    No. These tools organise qualitative data; they do not convert it into numbers. Counting code frequencies is possible but is a choice you make, and in interpretive traditions it is often inappropriate — say what you did and why.

    Will my university licence work on my personal laptop?

    Usually yes, for the duration of your registration and for academic use only. Terms vary, so check your IT services pages, and plan for the licence ending when your registration does.

    Is Taguette still maintained?

    Yes. Its repository showed activity in 2026 and the hosted service was accepting registrations at the time of writing. The stale release list on its GitHub mirror reflects where releases are published, not the state of the project.

    How many transcripts can Taguette handle?

    Enough for a typical interview-based doctoral project. The constraint you will meet is analytical rather than technical — the absence of attribute-based querying — so if you plan to compare coding systematically across participant groups, choose a fuller package from the start.

    Should I mention the software in my methods chapter?

    Yes, with the version number, alongside a full description of the analytic approach. The software belongs in a sentence; the method deserves several pages.

  • LaTeX vs Word for a PhD Thesis: Which Survives 80,000 Words and 300 References?

    LaTeX vs Word for a PhD Thesis: Which Survives 80,000 Words and 300 References?

    At 3,000 words this is a matter of taste. At 80,000 words, with 300 references, forty figures, cross-referenced chapters and a supervisor returning tracked comments on chapter four while you rewrite chapter six, it stops being a matter of taste. The two environments fail in different ways, and the right choice is the one whose failure mode you can live with.

    LaTeX Microsoft Word
    Cost Free (LaTeX Project Public Licence 1.3c) Commercial; normally via your institution
    Learning curve Steep at the start, flat afterwards Shallow at the start, steepens with document size
    Cross-references and numbering Automatic and reliable at any length Works, but can corrupt in very long documents
    Bibliography at 300 sources Excellent; style changed by editing one line Workable via a reference manager, slower and more fragile
    Mathematics Best in class Adequate; laborious at volume
    Supervisor collaboration Awkward unless they also use it Track changes is the sector’s default
    Stability at length Very high Degrades as figures and length accumulate
    Version control Native — plain text works with Git Possible but clumsy
    Typical strongholds Maths, physics, engineering, computer science, economics Humanities, social sciences, health, education, business

    The single question that decides it

    What does your supervisor use, and what does your department’s thesis template assume?

    This overrides every technical consideration below. A supervisor who cannot open your files, or who will not comment on a PDF, imposes a friction cost on every single round of feedback across three or four years. That compounds into far more lost time than any formatting advantage recovers.

    If your department publishes a LaTeX thesis class, that is a strong signal the local infrastructure supports it. If it publishes a Word template and nothing else, going your own way means solving formatting problems alone that everyone else has already had solved for them.

    The case for LaTeX

    LaTeX separates content from presentation. You mark up what something is — a chapter heading, a citation, a figure reference — and the system decides how it looks. At doctoral scale this produces several concrete advantages.

    Numbering and cross-references never break. Insert a new figure between figures 3.2 and 3.3 and every downstream number and every reference to them updates on the next compile. In a 250-page thesis revised repeatedly over a final year, this alone saves days.

    Bibliographies scale without complaint. Three hundred references are no harder than thirty, and switching citation style is a one-line change rather than a manual reformatting job.

    Mathematics is genuinely superior. If your thesis contains substantial mathematical content, this is close to decisive.

    It is plain text. Your thesis lives in files that work with Git, that diff meaningfully, and that will still open in fifty years. Version control on a thesis is an underrated safety net.

    It is free and open. LaTeX is distributed under the LaTeX Project Public Licence, currently version 1.3c. Nothing expires when your registration does.

    Overleaf removes most of the setup pain. Overleaf is a browser-based LaTeX editor that became part of Digital Science in 2014 and merged with ShareLaTeX in 2017. It runs collaboratively, so a supervisor can comment without installing anything. Many UK universities hold institutional licences — UCL, for instance, publicly announced a site licence making Professional-level accounts available to all its staff and students. Check whether yours does before paying for anything, and note that Overleaf’s campus-wide tier is now branded Commons AI rather than the older Overleaf Commons name you may find referenced in older guides.

    The case for Word

    Track changes is the sector’s shared language for feedback. This is the strongest practical argument, and it is a large one. Supervisors, examiners and proofreaders across most UK disciplines expect Word. Comment-and-accept workflows are frictionless in a way that PDF annotation on a LaTeX document is not.

    You already know it. Zero learning curve at the moment you most need to be writing rather than debugging.

    Reference managers integrate well. Zotero — a project of Digital Scholar, released under the GNU Affero General Public Licence version 3 — plugs into Word and handles citation insertion and bibliography generation competently, including at doctoral scale.

    Institutional support exists. Your IT service desk can help with Word. It almost certainly cannot help with a LaTeX compilation error at 11pm.

    Formatting corrections are immediate. If an examiner requires a change to layout during corrections, you make it and see it. In LaTeX you may be debugging a class file.

    A long thesis document with numbered chapters, figures and a bibliography
    Scale is the variable that changes the answer — everything that is trivial at 5,000 words is a system at 80,000.

    How each one fails

    Knowing the failure modes matters more than the feature lists, because you will meet one of them.

    LaTeX fails loudly and early. It refuses to compile, usually over a missing package, a stray brace or a misbehaving figure placement. This is frustrating but visible, and the error is almost always fixable with a search. Nothing is silently lost.

    Word fails quietly and late. The characteristic disaster is a long document with many embedded figures that becomes progressively slower, then starts renumbering incorrectly, corrupting a style, or losing a section break — and does so a fortnight before submission. It is quiet, it is late, and it is worse.

    Two mitigations make Word entirely viable at thesis scale, and most people who struggle have done neither. Write each chapter as a separate file and combine only at the end. Insert figures as linked images at final size rather than pasting them in and resizing. Do both and Word behaves.

    The recommendation

    Use what your supervisor and department use, unless your thesis is mathematics-heavy — in which case use LaTeX and find a way to make feedback work.

    If you are in a discipline where LaTeX is standard, use it: the infrastructure, templates and local expertise are all there. If you are in a discipline where Word is standard, use Word with per-chapter files and a reference manager. Switching to LaTeX mid-doctorate, in the writing-up year, is rarely a good trade — the time goes into learning rather than writing, at exactly the point you can least afford it.

    Whichever you choose, set up your reference manager on day one. The most expensive tooling mistake in a doctorate is not the editor; it is arriving at 250 sources with no bibliographic database and having to rebuild it by hand.

    Decide it once, early, and stop revisiting

    There is a recurring pattern worth naming: the candidate who reopens the tooling question every few months, trials a new environment, migrates two chapters, then reverts. This is almost always displacement activity dressed up as productivity, and it is most tempting precisely when the writing is going badly.

    Make the decision in your first term, using the supervisor-and-department test above, and then treat it as settled. The compounding advantage of either environment comes from accumulating templates, macros, styles and habits within it — all of which reset when you switch. A researcher who has used Word competently for four years will finish sooner than one who has used three environments expertly for eighteen months each.

    What neither will do

    Neither environment writes your argument. The chapter that decides whether the thesis passes is the one where you interpret your findings against the literature, and no template helps with that — our guide to writing the discussion chapter works through the moves it needs to make. Neither will tell you whether your claim is defensible, which is what our guide to stating an original contribution addresses. And neither helps with the progression milestones along the way, where the sample chapter you submit at the upgrade or confirmation review is being read for its argument rather than its typesetting.

    If the structure is clear and the drafting has stalled, you can build chapters in Tesify and move the text into whichever environment you have chosen. The scholarship stays 100% written by you.

    Frequently asked questions

    Can I write in LaTeX and give my supervisor a Word file?

    You can convert, but conversion is lossy and round-tripping edits back is painful. The better pattern is to send a PDF and agree how comments come back — many supervisors will annotate a PDF happily once asked. Agree this early rather than at the first feedback deadline.

    Is Overleaf free?

    There is a free tier, and paid tiers add collaborators and features. Many UK universities hold institutional licences that give staff and students an upgraded account at no personal cost, so check your IT services pages before subscribing.

    Will my examiners care which I used?

    No. They receive a PDF or a printed thesis and assess the research. What they will notice is inconsistent numbering, broken cross-references or a bibliography that does not match the citations — problems either tool can produce if handled carelessly.

    What about Google Docs?

    Excellent for collaborative drafting of short pieces, and genuinely poor as a thesis environment — it struggles with long documents, complex numbering and thesis-standard bibliography formatting. Draft in it if you like; do not assemble in it.

    Should I switch to LaTeX for my corrections?

    Almost never. Corrections have a deadline, and rebuilding a thesis in a new environment while making substantive changes is a way to introduce errors under time pressure. Make the corrections where the thesis already lives.

    How do I stop Word corrupting a long thesis?

    Keep chapters in separate files, use styles rather than manual formatting, insert images as linked files at final size, avoid nested tables and text boxes where you can, and keep dated backups. Most catastrophic Word failures at thesis scale trace back to one of these.

    Which reference manager works with both?

    Zotero handles both, integrating with Word and exporting the BibTeX files LaTeX needs. Whichever you choose, start using it from your first reading rather than retrofitting it later.

    Do I need to know LaTeX to use my department’s thesis class?

    Less than you might fear. A thesis class handles the layout, margins, front matter and numbering your institution requires, so your job is mostly writing text and marking up sections, citations and figures. Most candidates in LaTeX departments learn what they need within a fortnight and never touch the class file itself.

    How should I back up my thesis?

    In at least two places that are not the same physical machine, with version history rather than a single overwritten copy. Cloud storage with revision history, or a Git repository for LaTeX, protects against the failure that matters most — not losing the file, but discovering that you overwrote a good version with a worse one three weeks ago.

    Does it matter which one I use for journal articles from my thesis?

    It can. Some journals, particularly in mathematics, physics and computer science, supply LaTeX templates and prefer submissions in that format, while most others accept Word. If you expect to publish from your thesis, check the conventions in your target journals — writing the thesis in the same environment saves a conversion step later.