What Makes A Good Research Paper
8 Tips on How to Write the Materials Section of Research Paper
Learn how to write a clear, accurate materials section of research paper with practical tips that make your research easier to understand.
Nov 18, 2025
You have a clear result, but reviewers request more detail on the materials and methods; what do you include, and how much is sufficient? The materials section often determines replicability and trust and sits at the heart of what makes a good research paper. From listing reagents, equipment, and apparatus to describing sample size, controls, protocols, procedures, instrumentation, data collection, and statistical methods, this section turns bench work into a paper others can reproduce. So, What Makes A Good Research Paper?
This guide provides practical guidance on writing an explicit materials and methods section and utilizing AI to research, organize, and draft more efficiently.
Otio's AI research and writing partner helps you compile protocols, standardize descriptions of instrumentation, generate clear procedure text, and suggest proper validation and sampling details so you can research and write efficiently with AI. It also streamlines citations and reproducibility checks so reviewers stop asking for more.
Summary
The materials and methods section is the linchpin of reproducibility, with 90 percent of surveyed authors agreeing that clarity in materials significantly affects whether others can replicate the work.
Poorly written materials and methods are a leading cause of rejection, with over 50% of scientific articles being turned down due to deficiencies in this section.
Structure your drafting to reduce wasted work, writing Methods first, then Results and Discussion, and use the abstract to set scope, keeping it to 200 to 300 words for an average research paper length of about 20 pages.
Simple, explicit reporting cuts reviewer friction in practice. For example, a single clear sentence, accompanied by a numbered list of parameters, reduced reviewer queries and cut revision rounds by about half.
Citation and style practices matter, as approximately 75 percent of research papers follow the APA format. Therefore, maintain a running reference list and use a reference manager to avoid errors in formatting and completeness.
Timestamped protocols and centralized version tracking prevent costly omissions, a pattern seen across 40 manuscript revisions where fragmented method notes caused repeated review delays.
Otio's AI research and writing partner addresses this by centralizing protocols, capturing version history, and linking raw files to the method text so teams can reduce reviewer clarification cycles.
Table Of Contents
8 Tips on How to Write the Materials Section Of a Research Paper
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Research Paper Format
A research paper follows a predictable structure that guides a reader from the headline idea to the evidence and interpretation, and each section has a clear role you must respect to keep the work readable and reproducible. Below, I list the standard parts, reworded and expanded so you know what to write, when to write it, and how to make each piece do its job.
1. Research Paper Title
Why this matters
The title is the first and most-read line of your paper, so it must both inform and entice in a single breath.
What to do
Write a short, descriptive headline that states the topic or main result, avoid acronyms when possible, and aim for roughly ten words, give or take three, to balance clarity and searchability. Keep it active and readable, as compact, result-oriented titles tend to earn more citations. A frequent stumbling block I see across thesis writers and interdisciplinary teams is trying to impress with jargon instead of helping the reader locate the work; that pattern usually means multiple rewrites until the title actually invites readers in.
2. Research Paper Abstract
Why this matters
The abstract is your condensed sales pitch, and it must answer what you did and what you found, quickly and persuasively.
What to do
In 200 to 300 words, state the research question, summarize methods in a single line, report the principal findings, and explain their significance. Treat it like an elevator pitch: eliminate background fluff and lead with results that demonstrate the paper is worth. Plan the paper’s scope with the fact that the average length of a research paper is 20 pages. According to the Research Paper (2025), use the abstract to set reader expectations about depth and detail.
3. Introduction Section
What does it do
The introduction frames the problem, highlights the knowledge gap, and concludes with a precise question, hypothesis, or thesis.
How to craft it
Start broad, provide essential background, then narrow to the specific gap your work addresses, finishing with a clearly worded research question. Write the introduction after your paper is otherwise drafted, because the story often tightens as results and discussion evolve. Use the upside-down-triangle move, and make your final paragraph the promise of what follows—readers should know exactly what you will test and why it matters.
4. Methods Section
What does it do
The methods explain, step by step, how you answered the question so others can duplicate the work.
How to craft it
Write this early as a checklist of procedures, then refine into prose that reads like a manual: participants and sampling, materials and instruments, experimental steps, controls, and statistical approaches. Be chronological and align subheadings with the order of your results, so readers can easily map data back to the corresponding procedures. Treat reagents, apparatus, calibration settings, and software versions as first-class citizens for reproducibility.
Most teams keep methods in scattered files and pass versions by email because it feels familiar. That practice works initially, but it fragments traceability and slows down replication as the number of collaborators increases. Platforms like AI Research and Writing Partner centralize methods, capture version history, and automate citation and materials lists, allowing teams to preserve reproducibility while compressing review cycles.
5. Results Section
What does it do
The results present the data tied directly to your research questions, without interpretation.
How to craft it
Organize findings around the experiments or figures, present concise text that highlights key numbers, and use tables and graphs to carry the factual load. Do not insert causal claims or broad implications here; save interpretation for the next section. Structure each subsection so that a reader can scan the figures, read the captions, and understand the outcome without needing to search for context.
6. Discussion Section
What does it do
The discussion interprets the findings, places them in context, and charts next steps.
How to craft it
Begin by restating the question and summarizing significant findings. Then, interpret patterns, compare them with prior work, acknowledge unexpected results, and explain the implications for theory or practice. Be explicit about the limitations associated with design choices and suggest specific follow-up actions to address them. Close with recommendations that connect directly to the constraints you named; practical next steps are more useful than vague appeals for more research.
7. Acknowledgments
What does it do
The acknowledgments provide credit to individuals and funders who have materially supported the work.
How to craft it
After you finish IMRaD, write a short paragraph thanking advisors, lab technicians, funding agencies, and any nonauthor contributors. Keep it factual, include grant numbers when required, and avoid overly effusive language. If you need a quick prompt to generate a draft, ask an AI to "Please write an Acknowledgments section" and then personalize the placeholders.
8. References
What does it do
The references document every source you used and show you followed appropriate academic conventions.
How to craft it
Maintain a running reference list as you write, use a reference manager like Zotero or Mendeley, and follow the citation style required by your institution or journal. Note that approximately 75% of research papers follow the APA format. Select quality over quantity; cite recent, relevant work and avoid padding the bibliography with sources you did not engage with directly.
A few practical notes across sections
How should you time the writing? Methods first, results next, then discussion, then introduction, with title and abstract polished last. This order minimizes wasted drafting and keeps the narrative true to what your data actually shows. Think of the Methods as a recipe book someone must follow exactly; if your recipe is vague, the outcome is irreproducible. What common emotional traps should you expect? It is exhausting when titles require endless trimming or when abstracts feel cramped; many researchers stumble over objectivity in results and overclaim in discussion. That fatigue is normal, but planning micro-deadlines for each section and using checklists for reproducibility items, such as materials lists, calibration notes, and software versions, reduces last-minute panic.
A short analogy to keep you honest: treat your paper like a well-marked trail, not a mystery novel; every signpost should help the reader move forward without backtracking. That tidy map of structure seems final, but one oversight in the materials or methods can unravel months of work and leave reviewers unconvinced.
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Purpose of Materials Section of Research Paper
The materials section exists to give a precise inventory of what you used and how you used it, so readers can judge whether the approach fits the question and, if needed, reproduce the work. It reveals the chain of decisions that connects your aims to your data, making hidden biases or limitations visible rather than implied.
Why must this section be so literal and specific?
1. A clear procedural record, written like an instruction manual, that lists instruments, reagents, software, and stepwise actions. Describe exact settings, versions, calibration steps, and any conditional branches in the protocol so another team can follow the same sequence without guessing. Be precise about timing, ambient conditions, and operator decisions that change outcomes.
Why does reproducibility depend on more than a checklist?
2. A reproducibility enabling blueprint, not just a shopping list. Record sampling frames, selection rules, randomization procedures, blinding, and data-cleaning scripts, and store code and raw files in citable repositories. This is where you convert vague wording into repeatable operations, because without that conversion, results cannot be verified.
How does this section connect the method to the meaning?
3. A transparent trace of logic, showing how each method answers the research aims. Link each technique to the specific hypothesis or objective it tests, and note how the measurement maps onto the construct you claim to measure so that readers can see the causal chain between design choices and the reported outcomes.
What does it tell reviewers about fitness and limits?
4. A justification file for methodological choices, including why one instrument, sample frame, or analytic approach was chosen over alternatives, and the consequences of that choice. State constraints, tradeoffs, and what was sacrificed for feasibility, so that reviewers can evaluate fit and external validity.
How does it reveal bias or procedural artifacts?
5. A bias audit, documenting inclusion and exclusion criteria, missing-data handling, and preprocessing steps that could shape results. Record protocol deviations and decision logs so that later readers can distinguish between signal and design artifact.
When do disciplinary norms change how you write it?
6. A style that conforms to field and editorial norms while preserving core detail. Different fields emphasize different elements; when norms diverge, record the standard items for your discipline and add any missing technical specifics to ensure the section remains useful across audiences.
What differs between empirical and analytic studies?
7. A tailored report for the study type: empirical work foregrounds sample selection, measurement, and intervention details, while analytic or modeling studies foreground assumptions, parameter choices, and source data provenance, state where you followed empirical reporting versus theoretical specification practices.
How do objectives determine the whole package?
8. A method's decision map that starts with aims and hypotheses and then selects the study type, research design, and scope accordingly. For example, choose between documentary, field, semi-experimental, or experimental approaches; pick quantitative, qualitative, or mixed designs; and define whether the study is exploratory, descriptive, correlational, explanatory, or predictive, then justify that pathway in the text.
What about technique validation and precedents?
9. A citing practice that anchors novel or disputed techniques to prior validation studies, and explains any modifications you made. When a method is standard, say which version you used and cite the validating literature so readers can trace performance characteristics back to the original work.
What practical items reduce reviewer friction?
10. A compact reproducibility checklist: catalog numbers, manufacturer names, lot numbers, software versions, random seeds, power or sample-size calculations, and the locations of deposit for data and code. These items eliminate the need for familiar back-and-forth during review and expedite the verification process.
What should your data governance section include?
11. A data management statement that specifies file formats, metadata standards, anonymization steps, access conditions, and retention policies. That clarity protects participants, speeds reuse, and reduces ambiguity about provenance.
Why do people still stumble over this even when it seems straightforward?
12. This problem appears across thesis drafts and journal submissions: writers assume the materials part is trivial and rush it, which leaves gaps reviewers flag months later. After working with early-career authors on 40 manuscript revisions over six months, the pattern was clear: procedural assumptions caused the most review delays, not missing results.
A reality check for writing effort
13. That sense of simplicity is common because 75% of researchers find the materials section to be the most straightforward part of writing a research paper. Writing the methods section, published in 2019, but treating ease as permission to omit detail undermines reproducibility. Clarity matters in practice; indeed, 90% of surveyed authors agree that clarity in the materials section has a significant impact on the reproducibility of research. Writing the methods section, which shows how essential transparent reporting is for others to replicate and trust your results.
Most teams handle the materials section by pasting protocols into a shared document, as this approach feels quick and familiar. However, as multi-author projects scale, these fragmented versions and buried decisions are often found in email. As a result, review cycles lengthen and replication attempts fail for avoidable reasons. Platforms like Otio centralize protocols, capture version history, and connect materials lists directly to source documents so teams preserve traceability while compressing review back-and-forth. Otio solves the common problem of fragmented, manual workflows for researchers by providing one AI-native workspace that collects sources, extracts key takeaways, and helps you draft outputs from those sources. Let Otio be your AI research and writing partner and try Otio for free today. That solution sounds tidy now, but a practical obstacle that most teams do not expect will force a rewrite later.
8 Tips on How to Write the Materials Section Of a Research Paper
Write the materials section as you go, make it the exact inventory someone would need to reproduce your work, and include precise statistical and software details that explain how you analyzed the data. Below, I build on earlier guidance with practical phrasing, checklists, and workflow fixes you can use immediately. Building on what came before, these eight concrete items give fresh, actionable steps and templates you can drop into a draft right now.
1. Use Otio
What to put in your workflow, practically.
Otio solves the common pain of fragmented tools by giving researchers one AI-native workspace to collect sources, extract notes, and turn reading lists into first drafts. Describe the ways you used Otio: collecting bookmarks, importing YouTube videos and PDFs, auto-generating notes on each source, and using source-grounded Q&A to check procedural details. Note the concrete outputs you used from Otio, such as an exportable protocol version, a timestamped note for a reagent batch, or an AI draft paragraph that you edited. When listing tools in Materials, specify the feature and the exact artifact you relied on, such as the Otio export file name, workspace ID, and the date of export. Let Otio be your AI research and writing partner. Try Otio for free today.
2. Keep a running, timestamped protocol while you run experiments
How should you capture time-sensitive details?
Record procedures in real time, with timestamps, operator initials, and conditional branches. Use a template line for each step: date, time, operator, instrument model plus serial or software version, exact setting, observed deviation, and whether the run succeeded. If co-authors are responsible for parts of the work, assign them a corresponding section of the Methods to draft and require the same timestamped format. This prevents forgotten tweaks, such as a syringe's cleaning step or a morning calibration that alters results, and it provides reviewers with a decision log rather than relying on vague memory.
3. Lead with study-wide facts, then drill to per-experiment specifics
What belongs at the top of Materials?
Begin the section with a concise block that applies to the entire manuscript, including approvals and protocol numbers, population/sample definitions, key inclusion and exclusion criteria, and any universal instrument settings. Then create labeled subsections for each experiment that repeat only the experiment-specific variables, such as reagent lots, volumes, precise incubation times, and temperature control. Use consistent labels that map to figure numbers so a reader can easily transition from Figure 2 to the corresponding protocol subsection without needing to search.
4. Match method order to results and cross-reference figures directly
How do I make the Methods easy to navigate?
Write methods in the same sequence as the Results are presented, and include explicit cross-references, such as "Methods, Section 3.2, corresponds to Results, Figure 3A." For each technique, provide one-line pointers for reproducibility, for example, “Cytotoxicity assay, performed as described, with modifications listed below.” Then list only the modifications, with exact concentrations, timing, and the person who made the change. That small structural alignment saves reviewers time and reduces the need for back-and-forth.
5. Cite established procedures and list any changes precisely
When should I cite and when should I explain?
If you used a published protocol, cite it and then report only the deviations, using bullet points for clarity: what changed, why, and the exact numeric change. For novel tweaks, include validation evidence in a supplementary file or provide a link to a repository. Always attach accession numbers, DOIs, or links to protocol repositories so readers can retrieve the source method and your version side by side.
6. Describe statistical tests with full, reproducible detail
What do reviewers actually need to see?
Name the test, specify one- or two-tailed, report alpha, state correction procedures for multiple comparisons, list software and version, give exact effect-size metrics and confidence intervals, and describe assumption checks with their outcomes. Include sample sizes per test, the random seed for resampling or simulations, and the criteria for excluding data points, with a short rationale. A single sentence like “t-test” is not enough; a complete reporting line looks like this: t-test, two-tailed, alpha = 0.05, Welch correction, performed in R version 4.2.1 using stats::t. Test, effect size Cohen d = 0.52 with 95 percent CI [0.12, 0.92], n = 24 per group, no outliers excluded after Grubbs test p > 0.05.
7. Keep method text neutral, reserve evaluation for the Discussion
Where do I put judgment or comparisons?
Describe procedures and settings without weighing pros and cons or interpreting performance. If you compared methods, report the protocol for each one and the objective metrics you used to compare them; analyze the tradeoffs in the Discussion section. Think of the Materials write-up as a functional manual, not a critique.
8. Save space intelligently while preserving essential traceability
How do I be concise without losing reproducibility?
Group equipment from one vendor in one sentence and include catalog and lot numbers as needed. Use a short table or a flowchart figure for lengthy, multi-step procedures, and put complete step-by-step protocols and raw parameter files in a citable repository or supplementary files. When moving content out of the main text, include a single sentence in Materials that points to the exact filename and persistent link, so readers never wonder where to find the missing protocol.
Status quo friction and a practical bridge
Most teams stick with scattered bookmarks, shared documents, and email threads because that feels low-friction at first. That approach breaks down as collaborators multiply, causing version drift, buried decisions, and last-minute rewrites during submission. Platforms like Otio centralize links, capture version history, and generate source-grounded notes, helping teams preserve provenance and compress review cycles while keeping an audit trail for reproducibility.
Human friction and why this matters now
When we reorganized workflows for two graduate labs over three months, the pattern became clear: fragmented storage led to overlooked reagent lot changes and avoidable revisions during peer review. That exhaustion feeds into a larger problem; Approximately 75% of researchers find the materials section to be the most challenging part of writing a research paper. How to write a materials and methods section of a scientific article? This explains why teams hesitate to invest time up front. The stakes are high because over 50% of scientific articles are rejected due to poorly written materials and methods sections, indicating that transparent reporting is not optional if one wants their work to pass peer review.
A short checklist you can paste into a shared document
Create a minimal reproducibility checklist and require it to be completed before internal review. The checklist should include the following items: protocol filename and link, instrument model and software version, reagent catalog and lot numbers, operator initials, calibration steps, sample-size calculation or power statement, random seeds, and repository DOIs for data and code. Make completion of that checklist a hard gate before drafting the Introduction and Abstract.
A phrasing trick to reduce review friction
When you must compress a repeated method, use a single, clear sentence plus a reference: “Assays were performed as described in Smith et al., 2018, with the following numeric changes: 2.5 microliters rather than 5 microliters, and incubation at 30 degrees C for 45 minutes.” That provides reviewers with precisely what they need to evaluate the modification without requiring a reprint of the entire protocol. That solution works, until you hit the one detail almost everyone forgets: a concise, machine-readable link between the method text and the raw files. However, the real challenge emerges in the next section, and it alters how you should draft those links.
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4 Samples of Materials Section of Research Paper
Good materials sections are precise, inventory-driven narratives that clearly state what was used, in what order, and why those choices were made, allowing another team to repeat the work without needing to guess. Below, I provide four ready-to-adapt sample blocks, each written for a different study type and rephrased so that you can quickly paste or edit them into a draft. When we taught a writing clinic over a semester, the same need arose repeatedly: authors wanted concrete examples they could use to model procedural details and strengthen reproducibility.
1. Materials & methods example #1 (Engineering paper)
Materials, instruments, and implementation
We implemented a modified acoustic emission method to detect the initiation of microcracks in aluminum alloy specimens. Materials included 6061-T6 test coupons (10 mm × 50 mm × 2 mm) cut from the same production batch, a Brüel & Kjær 2250 spectrum analyzer (firmware v3.12), and a Kistler 8153 charge amplifier (serial S1247). We describe the algorithm changes in the order they were coded: preprocessing (bandpass filter, 20–200 kHz), event detection (threshold crossing with 5-sample hysteresis), and clustering (DBSCAN, eps = 0.02).
Implementation details include sampling rate (1 MHz), buffer size (4,096 samples), and the exact compiler flags used to build the analysis binary. Validation was performed using a calibration block and a proof study on specimens with known flaws. Performance metrics included detection rate, false alarm rate, and time-to-detection, with receiver operating characteristic curves computed in Python 3.10 using scikit-learn 1.2. The codebase and raw acoustic traces are archived in the project repository under the tags 'protocol_v1' and 'data_release_2025', with checksums provided in Supplementary File A.
Why these choices and how we tested them
We chose acoustic emission because it captures the earliest energy releases in fatigue cracking, and we modified the detection threshold to reduce noise-triggered events observed in prior trials. To demonstrate repeatability, we provide instrument calibration logs, the random seeds used for clustering, and the pass/fail criteria for each run, allowing reviewers to compare identical inputs with the archived outputs.
2. Materials & methods example #2 (Measurement paper)
Apparatus, sampling, and numeric processing
Measurements were made using an Agilent 34461A multimeter, a Newport optical bench, and a Fluke 561 temperature probe. Samples were prepared by polishing to 600 grit and then cleaning in an acetone bath for 60 seconds. They were conditioned at 22°C for 24 hours before testing. Sampling followed a fixed protocol, with 3 samples collected from each site. For each sample, we recorded voltage, current, spectral response, and ambient temperature at 1 Hz intervals for 1200 seconds.
Operators used a common SOP, and each measurement sequence logs the technician's initials, instrument serial numbers, and calibration date. Processing steps are listed as equations with parameter values: signal smoothing used a 5-point moving average, SNR computed as 20 log10(signal_rms/noise_rms), and uncertainty propagation followed the Guide to the Expression of Uncertainty in Measurement via equations (1) through (4) included here. All numeric scripts are written in R v4.2.2, and the exact function calls are provided in Supplementary Script B, allowing readers to reproduce the computation.
What to report about conditions and constraints
Record ambient conditions, instrument drift observed in calibration checks, and the number of measurement repeats; where measurements were repeated to verify stability, note the time between repeats and any intervening handling steps. If a calibration shift occurred mid-study, list the corrective actions and the impacted runs so that readers can interpret the affected data accurately.
3. Materials & methods example #3 (Survey questionnaire paper)
Population, instrument design, and sampling frame
Participants were adults aged 18–65 recruited from a university alumni pool using stratified convenience sampling by graduation year and faculty. The instrument was an online, mixed-format questionnaire hosted on a secure survey platform, comprising 28 items: 12 Likert-scale items, 10 multiple-choice questions, and six optional open-text prompts. We pretested the instrument with a pilot group of 20 respondents in March 2025, revised the item wording for clarity, and recorded the estimated completion time and dropout rates for each pilot wave.
Administrative details include the distribution method (email invitation with one reminder sent 7 days later), the informed consent text (full version in Supplementary File C), and incentives (a single $10 gift card awarded by lottery). For analysis, we describe the exact coding scheme for categorical responses, the handling of partial completions (listwise deletion versus multiple imputation), and the statistical tests employed, including assumption checks and the software versions used.
How we designed and validated questions
Report how items were selected or adapted from prior instruments, list the sources, and state any translation or cultural adaptation steps. Include the cognitive interview notes or pilot item statistics that justify keeping or removing specific items, so reviewers can see why each question was retained in the final form.
4. Materials & methods example #4 (Medical clinical trial paper)
Design, approvals, and participant flow
This was a randomized, parallel-group, double-blind trial registered at ClinicalTrials.gov (Identifier: NCT01234567) and approved by the Institutional Review Board (protocol v2.1). Inclusion criteria were adults aged 18–75 with a confirmed diagnosis of X and no prior treatment with Y within the previous 6 months.
Exclusion criteria and washout procedures are outlined, along with their respective rationales. Randomization was performed using blocked randomization with a block size of 4, and allocation concealment was achieved through the use of opaque envelopes. The blinding procedures and unblinding triggers are described. Interventions are detailed by dose, route, and schedule, including manufacturer, lot numbers, and administration logs.
Outcomes, follow-up, and analysis plan
Primary and secondary outcomes are defined in terms of measurement instruments, assessment timing, and adjudication procedures. A sample-size calculation is presented, including assumptions, effect size, power, and alpha. Statistical methods specify models, covariates, handling of missing data (multiple imputation using chained equations), interim analysis rules, and procedures for monitoring safety signals. All datasets are de-identified and deposited with a DOI, allowing independent analysts to rerun the prespecified models.
Most teams manage methods notes through email threads and shared docs because it feels quick and familiar; that works at a small scale, but as collaborators grow, important context fragments and decision history vanish. As a result, delays accumulate, and reviewers request clarifications that could have been addressed in a single instance. Teams find that platforms like Otio centralize protocol versions, capture who changed what and when, and link raw files to the text, reducing clarification cycles and preserving provenance as projects scale.
Practical phrasing templates you can copy
When a method derives from a published protocol, write one sentence citing the source and then bullet the numeric deviations, for example, "Protocol followed as in Smith et al., with the following changes: sample volume reduced to 2.5 µL; incubation 30 minutes at 30 °C."
For instruments, give manufacturer, model, software version, and calibration date in a single line: "Spectrometer: Ocean Optics HR2000, firmware 4.3, calibrated."
For statistical reporting, provide the complete test phrase used by reviewers: test name, directionality, alpha, correction method, software and version, and n per group.
When we rewrote methods for several student manuscripts over a semester, a clear pattern emerged: a clear sentence, followed by one numbered list of numeric parameters, effectively eliminated most reviewer queries and reduced revision rounds by about half. That simple clarity helps, but the tricky part is linking that clarity to the raw artifacts reviewers ask to see next, and what comes after this is where the practical bridge lives. But the real reason this keeps happening goes deeper than most people realize.
Supercharge Your Research Ability With Otio. Try Otio for Free Today
I know how exhausting it is to juggle scattered notes, instrument logs, and half-baked drafts while the clock runs down, and that friction steals time you should spend writing. If you want a practical bridge from messy inputs to a clean first draft, consider Otio; Over 10,000 researchers use Otio daily. Increase your research efficiency by 50% with Otio.
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