Item 1. Study design

For each experiment, provide brief details of study design including:

1a. The groups being compared, including control groups. If no control group has been used, the rationale should be stated.

Explanation. The choice of control or comparator group is dependent on the experimental objective. Negative controls are used to determine whether a difference between groups is caused by the intervention (e.g., wild-type animals versus genetically modified animals, placebo versus active treatment, sham surgery versus surgical intervention). Positive controls can be used to support the interpretation of negative results or determine if an expected effect is detectable.

It may not be necessary to include a separate control with no active treatment if, for example, the experiment aims to compare a treatment administered by different methods (e.g., intraperitoneal administration versus oral gavage) or animals that are used as their own control in a longitudinal study. A pilot study, such as one designed to test the feasibility of a procedure, might also not require a control group.

For complex study designs, a visual representation is more easily interpreted than a text description, so a timeline diagram or flowchart is recommended. Diagrams facilitate the identification of which treatments and procedures were applied to specific animals or groups of animals and at what point in the study these were performed. They also help to communicate complex design features such as whether factors are crossed or nested (hierarchical/multilevel designs), blocking (to reduce unwanted variation, see Item 4. Randomisation), or repeated measurements over time on the same experimental unit (repeated measures designs); see [16–18] for more information on different design types. The Experimental Design Assistant (EDA) is a platform to support researchers in the design of in vivo experiments; it can be used to generate diagrams to represent any type of experimental design [19].

For each experiment performed, clearly report all groups used. Selectively excluding some experimental groups (for example, because the data are inconsistent or conflict with the narrative of the paper) is misleading and should be avoided [20]. Ensure that test groups, comparators, and controls (negative or positive) can be identified easily. State clearly if the same control group was used for multiple experiments or if no control group was used.

Examples

Subitem 1a—Example 1

‘The DAV1 study is a one-way, two-period crossover trial with 16 piglets receiving amoxicillin and placebo at period 1 and only amoxicillin at period 2. Amoxicillin was administered orally with a single dose of 30 mg.kg^-1. Plasma amoxicillin concentrations were collected at same sampling times at each period: 0.5, 1, 1.5, 2, 4, 6, 8, 10 and 12 h’ [21].

Subitem 1a—Example 2

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Fig 1. Reproduced from reference [22].

https://doi.org/10.1371/journal.pbio.3000411.g001

1b. The experimental unit (e.g., a single animal, litter, or cage of animals).

Explanation. Within a design, biological and technical factors will often be organised hierarchically, such as cells within animals and mitochondria within cells, or cages within rooms and animals within cages. Such hierarchies can make determining the sample size difficult (is it the number of animals, cells, or mitochondria?). The sample size is the number of experimental units per group. The experimental unit is defined as the biological entity subjected to an intervention independently of all other units, such that it is possible to assign any two experimental units to different treatment groups. It is also sometimes called the unit of randomisation. In addition, the experimental units should not influence each other on the outcomes that are measured.

Commonly, the experimental unit is the individual animal, each independently allocated to a treatment group (e.g., a drug administered by injection). However, the experimental unit may be the cage or the litter (e.g., a diet administered to a whole cage, or a treatment administered to a dam and investigated in her pups), or it could be part of the animal (e.g., different drug treatments applied topically to distinct body regions of the same animal). Animals may also serve as their own controls, receiving different treatments separated by washout periods; here, the experimental unit is an animal for a period of time. There may also be multiple experimental units in a single experiment, such as when a treatment is given to a pregnant dam and then the weaned pups are allocated to different diets [23]. See [17,24,25] for further guidance on identifying experimental units.

Conflating experimental units with subsamples or repeated measurements can lead to artificial inflation of the sample size. For example, measurements from 50 individual cells from a single mouse represent n = 1 when the experimental unit is the mouse. The 50 measurements are subsamples and provide an estimate of measurement error and so should be averaged or used in a nested analysis. Reporting n = 50 in this case is an example of pseudoreplication [26]. It underestimates the true variability in a study, which can lead to false positives and invalidate the analysis and resulting conclusions [26,27]. If, however, each cell taken from the mouse is then randomly allocated to different treatments and assessed individually, the cell might be regarded as the experimental unit.

Clearly indicate the experimental unit for each experiment so that the sample sizes and statistical analyses can be properly evaluated.

Item 2. Sample size

2a. Specify the exact number of experimental units allocated to each group, and the total number in each experiment. Also indicate the total number of animals used.

Explanation. The sample size relates to the number of experimental units in each group at the start of the study and is usually represented by n (see Item 1. Study design for further guidance on identifying and reporting experimental units). This information is crucial to assess the validity of the statistical model and the robustness of the experimental results.

The sample size in each group at the start of the study may be different from the n numbers in the analysis (see Item 3. Inclusion and exclusion criteria); this information helps readers identify attrition or if there have been exclusions and in which group they occurred. Reporting the total number of animals used in the study is also useful to identify whether any were reused between experiments.

Report the exact value of n per group and the total number in each experiment (including any independent replications). If the experimental unit is not the animal, also report the total number of animals to help readers understand the study design. For example, in a study investigating diet using cages of animals housed in pairs, the number of animals is double the number of experimental units.

Example

Subitem 2a –example 1

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Fig 2. Reproduced from reference [32].

https://doi.org/10.1371/journal.pbio.3000411.g002

2b. Explain how the sample size was decided. Provide details of any a priori sample size calculation, if done.

Explanation. For any type of experiment, it is crucial to explain how the sample size was determined. For hypothesis-testing experiments, in which inferential statistics are used to estimate the size of the effect and to determine the weight of evidence against the null hypothesis, the sample size needs to be justified to ensure experiments are of an optimal size to test the research question [33,34] (see Item 13. Objectives). Sample sizes that are too small (i.e., underpowered studies) produce inconclusive results, whereas sample sizes that are too large (i.e., overpowered studies) raise ethical issues over unnecessary use of animals and may produce trivial findings that are statistically significant but not biologically relevant [35]. Low power has three effects: first, within the experiment, real effects are more likely to be missed; second, when an effect is detected, this will often be an overestimation of the true effect size [24]; and finally, when low power is combined with publication bias, there is an increase in the false positive rate in the published literature [36]. Consequently, low-powered studies contribute to the poor internal validity of research and risk wasting animals used in inconclusive research [37].

Study design can influence the statistical power of an experiment, and the power calculation used needs to be appropriate for the design implemented. Statistical programmes to help perform a priori sample size calculations exist for a variety of experimental designs and statistical analyses, both freeware (web-based applets and functions in R) and commercial software [38–40]. Choosing the appropriate calculator or algorithm to use depends on the type of outcome measures and independent variables, and the number of groups. Consultation with a statistician is recommended, especially when the experimental design is complex or unusual.

When the experiment tests the effect of an intervention on the mean of a continuous outcome measure, the sample size can be calculated a priori, based on a mathematical relationship between the predefined, biologically relevant effect size, variability estimated from prior data, chosen significance level, power, and sample size (see Box 3 and [17,41] for practical advice). If you have used an a priori sample size calculation, report

• the analysis method (e.g., two-tailed Student t test with a 0.05 significance threshold)
• the effect size of interest and a justification explaining why an effect size of that magnitude is relevant
• the estimate of variability used (e.g., standard deviation) and how it was estimated
• the power selected

There are several types of studies in which a priori sample size calculations are not appropriate. For example, the number of animals needed for antibody or tissue production is determined by the amount required and the production ability of an individual animal. For studies in which the outcome is the successful generation of a sample or a condition (e.g., the production of transgenic animals), the number of animals is determined by the probability of success of the experimental procedure.

In early feasibility or pilot studies, the number of animals required depends on the purpose of the study. When the objective of the preliminary study is primarily logistic or operational (e.g., to improve procedures and equipment), the number of animals needed is generally small. In such cases, power calculations are not appropriate and sample sizes can be estimated based on operational capacity and constraints [44]. Pilot studies alone are unlikely to provide adequate data on variability for a power calculation for future experiments. Systematic reviews and previous studies are more appropriate sources of information on variability [45].

If no power calculation was used to determine the sample size, state this explicitly and provide the reasoning that was used to decide on the sample size per group. Regardless of whether a power calculation was used or not, when explaining how the sample size was determined take into consideration any anticipated loss of animals or data, for example, due to exclusion criteria established upfront or expected attrition (see Item 3. Inclusion and exclusion criteria).

Item 3. Inclusion and exclusion criteria

3a. Describe any criteria used for including or excluding animals (or experimental units) during the experiment, and data points during the analysis. Specify if these criteria were established a priori. If no criteria were set, state this explicitly.

Explanation. Inclusion and exclusion criteria define the eligibility or disqualification of animals and data once the study has commenced. To ensure scientific rigour, the criteria should be defined before the experiment starts and data are collected [8,33,48,49]. Inclusion criteria should not be confused with animal characteristics (see Item 8. Experimental animals) but can be related to these (e.g., body weights must be within a certain range for a particular procedure) or related to other study parameters (e.g., task performance has to exceed a given threshold). In studies in which selected data are reanalysed for a different purpose, inclusion and exclusion criteria should describe how data were selected.

Exclusion criteria may result from technical or welfare issues such as complications anticipated during surgery or circumstances in which test procedures might be compromised (e.g., development of motor impairments that could affect behavioural measurements). Criteria for excluding samples or data include failure to meet quality control standards, such as insufficient sample volumes, unacceptable levels of contaminants, poor histological quality, etc. Similarly, how the researcher will define and handle data outliers during the analysis should also be decided before the experiment starts (see subitem 3b for guidance on responsible data cleaning).

Exclusion criteria may also reflect the ethical principles of a study in line with its humane endpoints (see Item 16. Animal care and monitoring). For example, in cancer studies, an animal might be dropped from the study and euthanised before the predetermined time point if the size of a subcutaneous tumour exceeds a specific volume [50]. If losses are anticipated, these should be considered when determining the number of animals to include in the study (see Item 2. Sample size). Whereas exclusion criteria and humane endpoints are typically included in the ethical review application, reporting the criteria used to exclude animals or data points in the manuscript helps readers with the interpretation of the data and provides crucial information to other researchers wanting to adopt the model.

Best practice is to include all a priori inclusion and exclusion/outlier criteria in a preregistered protocol (see Item 19. Protocol registration). At the very least, these criteria should be documented in a laboratory notebook and reported in manuscripts, explicitly stating that the criteria were defined before any data was collected.

3b. For each experimental group, report any animals, experimental units, or data points not included in the analysis and explain why. If there were no exclusions, state so.

Explanation. Animals, experimental units, or data points that are unaccounted for can lead to instances in which conclusions cannot be supported by the raw data [52]. Reporting exclusions and attritions provides valuable information to other investigators evaluating the results or who intend to repeat the experiment or test the intervention in other species. It may also provide important safety information for human trials (e.g., exclusions related to adverse effects).

There are many legitimate reasons for experimental attrition, some of which are anticipated and controlled for in advance (see subitem 3a on defining exclusion and inclusion criteria), but some data loss might not be anticipated. For example, data points may be excluded from analyses because of an animal receiving the wrong treatment, unexpected drug toxicity, infections or diseases unrelated to the experiment, sampling errors (e.g., a malfunctioning assay that produced a spurious result, inadequate calibration of equipment), or other human error (e.g., forgetting to switch on equipment for a recording).

Most statistical analysis methods are extremely sensitive to outliers and missing data. In some instances, it may be scientifically justifiable to remove outlying data points from an analysis, such as obvious errors in data entry or measurement with readings that are outside a plausible range. Inappropriate data cleaning has the potential to bias study outcomes [53]; providing the reasoning for removing data points enables the distinction to be made between responsible data cleaning and data manipulation. Missing data, common in all areas of research, can impact the sensitivity of the study and also lead to biased estimates, distorted power, and loss of information if the missing values are not random [54]. Analysis plans should include methods to explore why data are missing. It is also important to consider and justify analysis methods that account for missing data [55,56].

There is a movement toward greater data sharing (see Item 20. Data access), along with an increase in strategies such as code sharing to enable analysis replication. These practices, however transparent, still need to be accompanied by a disclosure on the reasoning for data cleaning and whether methods were defined before any data were collected.

Report all animal exclusions and loss of data points, along with the rationale for their exclusion. For example, this information can be summarised as a table or a flowchart describing attrition in each treatment group. Accompanying this information should be an explicit description of whether researchers were blinded to the group allocations when data or animals were excluded (see Item 5. Blinding and [57]). Explicitly state when built-in models in statistics packages have been used to remove outliers (e.g., GraphPad Prism’s outlier test).

Examples

Subitem 3b—Example 1

‘Pen was the experimental unit for all data. One entire pen (ZnAA90) was removed as an outlier from both Pre-RAC and RAC periods for poor performance caused by illness unrelated to treatment…. Outliers were determined using Cook’s D statistic and removed if Cook’s D > 0.5. One steer was determined to be an outlier for day 48 liver biopsy TM and data were removed’ [58].

Subitem 3b—Example 2

‘Seventy-two SHRs were randomized into the study, of which 13 did not meet our inclusion and exclusion criteria because the drop in cerebral blood flow at occlusion did not reach 60% (seven animals), postoperative death (one animal: autopsy unable to identify the cause of death), haemorrhage during thread insertion (one animal), and disconnection of the silicon tip of the thread during withdrawal, making the permanence of reperfusion uncertain (four animals). A total of 59 animals were therefore included in the analysis of infarct volume in this study. In error, three animals were sacrificed before their final assessment of neurobehavioral score: one from the normothermia/water group and two from the hypothermia/pethidine group. These errors occurred blinded to treatment group allocation. A total of 56 animals were therefore included in the analysis of neurobehavioral score’ [51].

Subitem 3b—Example 3

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Fig 3. Reproduced from reference [59].

https://doi.org/10.1371/journal.pbio.3000411.g003

3c. For each analysis, report the exact value of n in each experimental group.

Explanation. The exact number of experimental units analysed in each group (i.e., the n number) is essential information for the reader to interpret the analysis; it should be reported unambiguously. All animals and data used in the experiment should be accounted for in the data presented. Sometimes, for good reasons, animals may need to be excluded from a study (e.g., illness or mortality), or data points excluded from analyses (e.g., biologically implausible values). Reporting losses will help the reader to understand the experimental design process, replicate methods, and provide adequate tracking of animal numbers in a study, especially when sample size numbers in the analyses do not match the original group numbers.

For each outcome measure, indicate numbers clearly within the text or on figures and provide absolute numbers (e.g., 10/20, not 50%). For studies in which animals are measured at different time points, explicitly report the full description of which animals undergo measurement and when [33].

Item 4. Randomisation

4a. State whether randomisation was used to allocate experimental units to control and treatment groups. If done, provide the method used to generate the randomisation sequence.

Explanation. Using appropriate randomisation methods during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment and provides balanced numbers in each treatment group. Selecting an animal ‘at random’ (i.e., haphazardly or arbitrarily) from a cage is not statistically random, as the process involves human judgement. It can introduce bias that influences the results, as a researcher may (consciously or subconsciously) make judgements in allocating an animal to a particular group, or because of unknown and uncontrolled differences in the experimental conditions or animals in different groups. Using a validated method of randomisation helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups [62–64]. Inferential statistics based on nonrandomised group allocation are not valid [65,66]. Thus, the use of randomisation is a prerequisite for any experiment designed to test a hypothesis. Examples of appropriate randomisation methods include online random number generators (e.g., https://www.graphpad.com/quickcalcs/randomize1/) or a function like Rand() in spreadsheet software such as Excel, Google Sheets, or LibreOffice. The EDA has a dedicated feature for randomisation and allocation concealment [19].

Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding are more likely to report exaggerated effects that meet conventional measures of statistical significance [67–69]. It is especially important to use randomisation in situations in which it is not possible to blind all or parts of the experiment, but even with randomisation, researcher bias can pervert the allocation. This can be avoided by using allocation concealment (see Item 5. Blinding). In studies in which sample sizes are small, simple randomisation may result in unbalanced groups; here, randomisation strategies to balance groups such as randomising in matched pairs [70–72] and blocking are encouraged [17]. Reporting the precise method used to allocate animals or experimental units to groups enables readers to assess the reliability of the results and identify potential limitations.

Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see Box 4), the method used to generate the randomisation sequence (e.g., computer-generated randomisation sequence, with details of the algorithm or programme used), and what was randomised (e.g., treatment to experimental unit, order of treatment for each animal). If this varies between experiments, report this information specifically for each experiment. If randomisation was not the method used to allocate experimental units to groups, state this explicitly and explain how the groups being compared were formed.

4b. Describe the strategy used to minimise potential confounders such as the order of treatments and measurements, or animal/cage location. If confounders were not controlled, state this explicitly.

Explanation. Ensuring there is no systematic difference between animals in different groups apart from the experimental exposure is an important principle throughout the conduct of the experiment. Identifying nuisance variables (sources of variability or conditions that could potentially bias results) and managing them in the design and analysis increases the sensitivity of the experiment. For example, rodents in cages at the top of the rack may be exposed to higher light levels, which can affect stress [77].

Reporting the strategies implemented to minimise potential differences that arise between treatment groups during the course of the experiment enables others to assess the internal validity. Strategies to report include standardising (keeping conditions the same, e.g., all surgeries done by the same surgeon), randomising (e.g., the sampling or measurement order), and blocking or counterbalancing (e.g., position of animal cages or tanks on the rack), to ensure groups are similarly affected by a source of variability. In some cases, practical constraints prevent some nuisance variables from being randomised, but they can still be accounted for in the analysis (see Item 7. Statistical methods).

Report the methods used to minimise confounding factors alongside the methods used to allocate animals to groups. If no measures were used to minimise confounders (e.g., treatment order, measurement order, cage or tank position on a rack), explicitly state this and explain why.

Item 5. Blinding

Describe who was aware of the group allocation at the different stages of the experiment (during the allocation, the conduct of the experiment, the outcome assessment, and the data analysis).

Explanation. Researchers often expect a particular outcome and can unintentionally influence the experiment or interpret the data in such a way as to support their preferred hypothesis [81]. Blinding is a strategy used to minimise these subjective biases.

Although there is primary evidence of the impact of blinding in the clinical literature that directly compares blinded versus unblinded assessment of outcomes [82], there is limited empirical evidence in animal research [83,84]. There are, however, compelling data from systematic reviews showing that nonblinded outcome assessment leads to the treatment effects being overestimated, and the lack of bias-reducing measures such as randomisation and blinding can contribute to as much as 30%–45% inflation of effect sizes [67,68,85].

Ideally, investigators should be unaware of the treatment(s) animals have received or will be receiving, from the start of the experiment until the data have been analysed. If this is not possible for every stage of an experiment (see Box 5), it should always be possible to conduct at least some of the stages blind. This has implications for the organisation of the experiment and may require help from additional personnel—for example, a surgeon to perform interventions, a technician to code the treatment syringes for each animal, or a colleague to code the treatment groups for the analysis. Online resources are available to facilitate allocation concealment and blinding [19].

Specify whether blinding was used or not for each step of the experimental process (see Box 5) and indicate what particular treatment or condition the investigators were blinded to, or aware of.

If blinding was not used at any of the steps outlined in Box 5, explicitly state this and provide the reason why blinding was not possible or not considered.

Item 6. Outcome measures

6a. Clearly define all outcome measures assessed (e.g., cell death, molecular markers, or behavioural changes).

Explanation. An outcome measure (also known as a dependent variable or a response variable) is any variable recorded during a study (e.g., volume of damaged tissue, number of dead cells, specific molecular marker) to assess the effects of a treatment or experimental intervention. Outcome measures may be important for characterising a sample (e.g., baseline data) or for describing complex responses (e.g., ‘haemodynamic’ outcome measures including heart rate, blood pressure, central venous pressure, and cardiac output). Failure to disclose all the outcomes that were measured introduces bias in the literature, as positive outcomes (e.g., those statistically significant) are reported more often [88–91].

Explicitly describe what was measured, especially when measures can be operationalised in different ways. For example, activity could be recorded as time spent moving or distance travelled. When possible, the recording of outcome measures should be made in an unbiased manner (e.g., blinded to the treatment allocation of each experimental group; see Item 5. Blinding). Specify how the outcome measure(s) assessed are relevant to the objectives of the study.

6b. For hypothesis-testing studies, specify the primary outcome measure, i.e., the outcome measure that was used to determine the sample size.

Explanation. In a hypothesis-testing experiment, the primary outcome measure answers the main biological question. It is the outcome of greatest importance, identified in the planning stages of the experiment and used as the basis for the sample size calculation (see Box 3). For exploratory studies, it is not necessary to identify a single primary outcome, and often multiple outcomes are assessed (see Item 13. Objectives).

In a hypothesis-testing study powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects of the intervention, but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable [88,93]. Studies that claim to test a hypothesis but do not specify a predefined primary outcome measure or those that change the primary outcome measure after data were collected (also known as primary outcome switching) are liable to selectively report only statistically significant results, favouring more positive findings [94].

Registering a protocol in advance protects the researcher against concerns about selective outcome reporting (also known as data dredging or p-hacking) and provides evidence that the primary outcome reported in the manuscript accurately reflects what was planned [95] (see Item 19. Protocol registration).

In studies using inferential statistics to test a hypothesis (e.g., t test, ANOVA), if more than one outcome was assessed, explicitly identify the primary outcome measure, state whether it was defined as such prior to data collection and whether it was used in the sample size calculation. If there was no primary outcome measure, explicitly state so.

Item 7. Statistical methods

7a. Provide details of the statistical methods used for each analysis, including software used.

Explanation. The statistical analysis methods implemented will reflect the goals and the design of the experiment; they should be decided in advance before data are collected (see Item 19. Protocol registration). Both exploratory and hypothesis-testing studies might use descriptive statistics to summarise the data (e.g., mean and SD, or median and range). In exploratory studies in which no specific hypothesis was tested, reporting descriptive statistics is important for generating new hypotheses that may be tested in subsequent experiments, but it does not allow conclusions beyond the data. In addition to descriptive statistics, hypothesis-testing studies might use inferential statistics to test a specific hypothesis.

Reporting the analysis methods in detail is essential to ensure readers and peer reviewers can assess the appropriateness of the methods selected and judge the validity of the output. The description of the statistical analysis should provide enough detail so that another researcher could reanalyse the raw data using the same method and obtain the same results. Make it clear which method was used for which analysis.

Analysing the data using different methods and selectively reporting those with statistically significant results constitutes p-hacking and introduces bias in the literature [90,94]. Report all analyses performed in full. Relevant information to describe the statistical methods include

• the outcome measures
• the independent variables of interest
• the nuisance variables taken into account in each statistical test (e.g., as blocking factors or covariates)
• what statistical analyses were performed and references for the methods used
• how missing values were handled
• adjustment for multiple comparisons
• the software package and version used, including computer code if available [97]

The outcome measure is potentially affected by the treatments or interventions being tested but also by other factors, such as the properties of the biological samples (sex, litter, age, weight, etc.) and technical considerations (cage, time of day, batch, experimenter, etc.). To reduce the risk of bias, some of these factors can be taken into account in the design of the experiment, for example, by using blocking factors in the randomisation (see Item 4. Randomisation). Factors deemed to affect the variability of the outcome measure should also be handled in the analysis, for example, as a blocking factor (e.g., batch of reagent or experimenter) or as a covariate (e.g., starting tumour size at point of randomisation).

Furthermore, to conduct the analysis appropriately, it is important to recognise the hierarchy that can exist in an experiment. The hierarchy can induce a clustering effect; for example, cage, litter, or animal effects can occur when the outcomes measured for animals from the same cage/litter, or for cells from the same animal, are more similar to each other. This relationship has to be managed in the statistical analysis by including cage/litter/animal effects in the model or by aggregating the outcome measure to the cage/litter/animal level. Thus, describing the reality of the experiment and the hierarchy of the data, along with the measures taken in the design and the analysis to account for this hierarchy, is crucial to assessing whether the statistical methods used are appropriate.

For bespoke analysis—for example, regression analysis with many terms—it is essential to describe the analysis pipeline in detail. This could include detailing the starting model and any model simplification steps.

When reporting descriptive statistics, explicitly state which measure of central tendency is reported (e.g., mean or median) and which measure of variability is reported (e.g., standard deviation, range, quartiles, or interquartile range). Also describe any modification made to the raw data before analysis (e.g., relative quantification of gene expression against a housekeeping gene). For further guidance on statistical reporting, refer to the Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines [98].

7b. Describe any methods used to assess whether the data met the assumptions of the statistical approach, and what was done if the assumptions were not met.

Explanation. Hypothesis tests are based on assumptions about the underlying data. Describing how assumptions were assessed and whether these assumptions are met by the data enables readers to assess the suitability of the statistical approach used. If the assumptions are incorrect, the conclusions may not be valid. For example, the assumptions for data used in parametric tests (such as a t test, z test, ANOVA, etc.) are that the data are continuous, the residuals from the analysis are normally distributed, the responses are independent, and different groups have similar variances.

There are various tests for normality, for example, the Shapiro-Wilk and Kolmogorov-Smirnov tests. However, these tests have to be used cautiously. If the sample size is small, they will struggle to detect non-normality; if the sample size is large, the tests will detect unimportant deviations. An alternative approach is to evaluate data with visual plots, e.g., normal probability plots, box plots, scatterplots. If the residuals of the analysis are not normally distributed, the assumption may be satisfied using a data transformation in which the same mathematical function is applied to all data points to produce normally distributed data (e.g., log_e, log₁₀, square root).

Other types of outcome measures (binary, categorical, or ordinal) will require different methods of analysis, and each will have different sets of assumptions. For example, categorical data are summarised by counts and percentages or proportions and are analysed by tests of proportions; these analysis methods assume that data are binary, ordinal or nominal, and independent [18].

For each statistical test used (parametric or nonparametric), report the type of outcome measure and the methods used to test the assumptions of the statistical approach. If data were transformed, identify precisely the transformation used and which outcome measures it was applied to. Report any changes to the analysis if the assumptions were not met and an alternative approach was used (e.g., a nonparametric test was used, which does not require the assumption of normality). If the relevant assumptions about the data were not tested, state this explicitly.

Item 8. Experimental animals

8a. Provide species-appropriate details of the animals used, including species, strain and substrain, sex, age or developmental stage, and, if relevant, weight.

Explanation. The species, strain, substrain, sex, weight, and age of animals are critical factors that can influence most experimental results [103–107]. Reporting the characteristics of all animals used is equivalent to standardised human patient demographic data; these data support both the internal and external validity of the study results. It enables other researchers to repeat the experiment and generalise the findings. It also enables readers to assess whether the animal characteristics chosen for the experiment are relevant to the research objectives.

When reporting age and weight, include summary statistics for each experimental group (e.g., mean and standard deviation) and, if possible, baseline values for individual animals (e.g., as supplementary information or a link to a publicly accessible data repository). As body weight might vary during the course of the study, indicate when the measurements were taken. For most species, precise reporting of age is more informative than a description of the developmental status (e.g., a mouse referred to as an adult can vary in age from 6 to 20 weeks [108]). In some cases, however, reporting the developmental stage is more informative than chronological age—for example, in juvenile Xenopus, in which rate of development can be manipulated by incubation temperature [109].

Reporting the weight or the sex of the animals used may not feasible for all studies. For example, sex may be unknown for embryos or juveniles, or weight measurement may be particularly stressful for some aquatic species. If reporting these characteristics can be reasonably expected for the species used and the experimental setting but are not reported, provide a justification.

8b. Provide further relevant information on the provenance of animals, health/immune status, genetic modification status, genotype, and any previous procedures.

Explanation. The animals’ provenance, their health or immune status, and their history of previous testing or procedures can influence their physiology and behaviour, as well as their response to treatments, and thus impact on study outcomes. For example, animals of the same strain but from different sources, or animals obtained from the same source but at different times, may be genetically different [16]. The immune or microbiological status of the animals can also influence welfare, experimental variability, and scientific outcomes [112–114].

Report the health status of all animals used in the study and any previous procedures the animals have undergone. For example, if animals are specific pathogen free (SPF), list the pathogens that they were declared free of. If health status is unknown or was not tested, explicitly state this.

For genetically modified animals, describe the genetic modification status (e.g., knockout, overexpression), genotype (e.g., homozygous, heterozygous), manipulated gene(s), genetic methods and technologies used to generate the animals, how the genetic modification was confirmed, and details of animals used as controls (e.g., littermate controls [115]).

Reporting the correct nomenclature is crucial to understanding the data and ensuring that the research is discoverable and replicable [116–118]. Useful resources for reporting nomenclature for different species include

• Mice—International Committee on Standardized Genetic Nomenclature (https://www.jax.org/jax-mice-and-services/customer-support/technical-support/genetics-and-nomenclature)
• Rats—Rat Genome and Nomenclature Committee (https://rgd.mcw.edu/)
• Zebrafish—Zebrafish Information Network (http://zfin.org/)
• Xenopus—Xenbase (http://www.xenbase.org/entry/)
• Drosophila—FlyBase (http://flybase.org/)
• C. elegans—WormBase (https://wormbase.org/)

Item 9. Experimental procedures

For each experimental group, including controls, describe the procedures in enough detail to allow others to replicate them, including:

9a. What was done, how it was done, and what was used.

Explanation. Essential information to describe in the manuscript includes the procedures used to develop the model (e.g., induction of the pathology), the procedures used to measure the outcomes, and pre- and postexperimental procedures, including animal handling, welfare monitoring, and euthanasia. Animal handling can be a source of stress, and the specific method used (e.g., mice picked up by tail or in cupped hands) can affect research outcomes [61,121,122]. Details about animal care and monitoring intrinsic to the procedure are discussed in further detail in Item 16. Animal care and monitoring. Provide enough detail to enable others to replicate the methods and highlight any quality assurance and quality control used [123,124]. A schematic of the experimental procedures with a timeline can give a clear overview of how the study was conducted. Information relevant to distinct types of interventions and resources are described in Table 1.

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Table 1. Examples of information to include when reporting specific types of experimental procedures and resources.

https://doi.org/10.1371/journal.pbio.3000411.t001

When available, cite the Research Resource Identifier (RRID) for reagents and tools used [126,127]. RRIDs are unique and stable, allowing unambiguous identification of reagents or tools used in a study, aiding other researchers to replicate the methods.

Detailed step-by-step procedures can also be saved and shared online, for example, using Protocols.io [128], which assigns a digital object identifier (DOI) to the protocol and allows cross-referencing between protocols and publications.

Examples

Subitem 9a—Example 1

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Fig 4. This figure is an alternative version of the figure published in reference [129].

https://doi.org/10.1371/journal.pbio.3000411.g004

Subitem 9a—Example 2

‘For the diet-induced obesity (DIO) model, eight-week-old male mice had ad libitum access to drinking water and were kept on standard chow (SFD, 10.9 kJ/g) or on western high-fat diet (HFD; 22 kJ/g; kcal from 42% fat, 43% from carbohydrates and 15% from protein; E15721-34, Ssniff, Soest, Germany) for 15 weeks (https://dx.doi.org/10.17504/protocols.io.kbacsie)’ [130].

Subitem 9a—Example 3

‘The frozen kidney tissues were lysed. The protein concentration was determined with the Pierce BCA assay kit (catalogue number 23225; Thermo Fisher Scientific, Rockford, IL, USA). A total of 100–150 μg total proteins were resolved on a 6–12% SDS-PAGE gel. The proteins were then transferred to a nitrocellulose membrane, blocked with 5% skimmed milk for 1 h at room temperature and incubated overnight at 4°C with primary antibodies against the following proteins: proliferating cell nuclear antigen (PCNA; Cat# 2586, RRID: AB_2160343), phospho-AMPK (Cat# 2531, RRID: AB_330330), phospho-mTOR (Cat# 2971, RRID: AB_330970)…. The β-actin (Cat# A5441, RRID: AB_476744) antibody was obtained from Sigma. The blots were subsequently probed with HRP-conjugated anti-mouse (Cat# A0216) or anti-rabbit IgG (Cat# A0208; Beyotime Biotechnology, Beijing, China) at 1:1000. Immunoreactive bands were visualized by enhanced chemiluminescence, and densitometry was performed using ImageJ software (RRID: SCR_003070, Bio-Rad Laboratories)’ [131].

9b. When and how often.

Explanation. Clearly report the frequency and timing of experimental procedures and measurements, including the light and dark cycle (e.g., 12L:12D), circadian time cues (e.g., lights on at 8:00 AM), and experimental time sequence (e.g., interval between baseline and comparator measurements or interval between procedures and measurements). Along with innate circadian rhythms, these can affect research outcomes such as behavioural, physiological, and immunological parameters [132,133]. Also report the timing and frequency of welfare assessments, taking into consideration the normal activity patterns (see Item 16. Animal care and monitoring). For example, nocturnal animals may not show behavioural signs of discomfort during the day [134].

If the timing of procedures or measurements varies between animals, this information can be provided as a supplementary table listing each animal.

9c. Where (including detail of any acclimatisation periods).

Explanation. Physiological acclimatisation after a stressful event, such as transport (e.g., between supplier, animal facility, operating theatre, and laboratory), but before the experiment begins allows stabilisation of physiological responses of the animal [137,138]. Protocols vary depending on species, strain, and outcome; for example, physiological acclimatisation following transportation of different animals can take anywhere from 24 hours to more than 1 week [139]. Procedural acclimatisation immediately before a procedure allows stabilisation of the animals’ responses after unaccustomed handling, novel environments, and previous procedures, which otherwise can induce behavioural and physiological changes [140,141]. Standard acclimatisation periods may vary between research laboratories, and this information cannot be inferred by readers.

Indicate where studies were performed (e.g., dedicated laboratory space or animal facility, home cage, open field arena, water maze) and whether periods of physiological or procedural acclimatisation were included in the study protocol, including type and duration. If the study involved multiple sites, explicitly state where each experiment and sample analysis was performed. Include any accreditation of laboratories if appropriate (e.g., if samples were sent to a commercial laboratory for analysis).

9d. Why (provide rationale for procedures).

Explanation. There may be numerous approaches to investigate any given research problem; therefore, it is important to explain why a particular procedure or technique was chosen. This is especially relevant when procedures are novel or specific to a research laboratory or constrained by the animal model or experimental equipment (e.g., route of administration determined by animal size [143]).

Item 10. Results

For each experiment conducted, including independent replications, report:

10a. Summary/descriptive statistics for each experimental group, with a measure of variability where applicable (e.g., mean and SD, or median and range).

Explanation. Summary/descriptive statistics provide a quick and simple description of the data; they communicate quantitative results easily and facilitate visual presentation. For continuous data, these descriptors include a measure of central tendency (e.g., mean, median) and a measure of variability (e.g., quartiles, range, standard deviation) to help readers assess the precision of the data collected. Categorical data can be expressed as counts, frequencies, or proportions.

Report data for all experiments conducted. If a complete experiment is repeated on a different day or under different conditions, report the results of all repeats rather than selecting data from representative experiments. Report the exact number of experimental units per group so readers can gauge the reliability of the results (see Item 2. Sample size and Item 3. Inclusion and exclusion criteria). Present data clearly as text, in tables, or in graphs, to enable information to be evaluated or extracted for future meta-analyses [146]. Report descriptive statistics with a clearly identified measure of variability for each group. Fig 5 shows data summarised as means and standard deviations and, in brackets, ranges. Box plots are a convenient way to summarise continuous data, plotted as median and interquartile range, as shown in Fig 6.

Examples

Subitem 10a—Example 1

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Fig 5. Reproduced from reference [147].

https://doi.org/10.1371/journal.pbio.3000411.g005

Subitem 10a—Example 2

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Fig 6. Reproduced from reference [148].

https://doi.org/10.1371/journal.pbio.3000411.g006

10b. If applicable, the effect size with a confidence interval.

Explanation. In hypothesis-testing studies using inferential statistics, investigators frequently confuse statistical significance and small p-values with biological or clinical importance [149]. Statistical significance is usually quantified and evaluated against a preassigned threshold, with p < 0.05 often used as a convention. However, statistical significance is heavily influenced by sample size and variation in the data (see Item 2. Sample size). Investigators must consider the size of the effect that was observed and whether this is a biologically relevant change.

Effect sizes are often not reported in animal research, but they are relevant to both exploratory and hypothesis-testing studies. An effect size is a quantitative measure that estimates the magnitude of differences between groups or strength of relationships between variables. It can be used to assess the patterns in the data collected and make inferences about the wider population from which the sample came. The confidence interval for the effect indicates how precisely the effect has been estimated and tells the reader about the strength of the effect [150]. In studies in which statistical power is low and/or hypothesis-testing is inappropriate, providing the effect size and confidence interval indicates how small or large an effect might really be, so a reader can judge the biological significance of the data [151,152]. Reporting effect sizes with confidence intervals also facilitates extraction of useful data for systematic review and meta-analysis. When multiple independent studies included in a meta-analysis show quantitatively similar effects, even if each is statistically nonsignificant, this provides powerful evidence that a relationship is ‘real’, although small.

Report all analyses performed, even those providing non-statistically significant results. Report the effect size to indicate the size of the difference between groups in the study, with a confidence interval to indicate the precision of the effect size estimate.

Example

Subitem 10b—Example 1

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Fig 7. Reproduced from reference [29].

https://doi.org/10.1371/journal.pbio.3000411.g007

Item 11. Abstract

Provide an accurate summary of the research objectives, animal species, strain and sex, key methods, principal findings, and study conclusions.

Explanation. A transparent and accurate abstract increases the utility and impact of the manuscript and allows readers to assess the reliability of the study [153]. The abstract is often used as a screening tool by readers to decide whether to read the full article or whether to select an article for inclusion in a systematic review. However, abstracts often either do not contain enough information for this purpose [11] or contain information that is inconsistent with the results in the rest of the manuscript [154,155]. In systematic reviews, initial screens to identify papers are based on titles, abstracts, and keywords [156]. Leaving out of the abstract information such as the species of animal used or the drugs being tested limits the value of preclinical systematic reviews as relevant studies cannot be identified and included. For example, in a systematic review of the effect of the MVA85A vaccine on tuberculosis challenge in animals, the largest preclinical trial did not include the vaccine name in the abstract or keywords of the publication; the paper was only included in the systematic review following discussions with experts in the field [157].

To maximise utility, include details of the species, sex, and strain of animals used and accurately report the methods, results, and conclusions of the study. Also describe the objectives of the study, including whether it was designed either to test a specific hypothesis or to generate a new hypothesis (see Item 13. Objectives). Incorporating this information will enable readers to interpret the strength of evidence and judge how the study fits within the wider knowledge base.

Item 12. Background

12a. Include sufficient scientific background to understand the rationale and context for the study, and explain the experimental approach.

Explanation. Scientific background information for an animal study should demonstrate a clear evidence gap and explain why an in vivo approach was warranted. Systematic reviews of the animal literature provide the most convincing evidence that a research question has not been conclusively addressed, by showing the extent of current evidence within a field of research. They can also inform the choice of animal model by providing a comprehensive overview of the models used along with their benefits and limitations [160–162].

Describe the rationale and context of the study and how it relates to other research, including relevant references to previous work. Outline evidence underpinning the hypothesis or objectives and explain why the experimental approach is best suited to answer the research question.

12b. Explain how the animal species and model used address the scientific objectives and, where appropriate, the relevance to human biology.

Explanation. Provide enough detail for the reader to assess the suitability of the animal model used to address the research question. Include information on the rationale for choosing a particular species and explain how the outcome measures assessed are relevant to the condition under study and how the model was validated. Stating that an animal model is commonly used in the field is not appropriate, and a well-considered, detailed rationale should be provided.

When the study models an aspect of a human disease, indicate how the model is appropriate for addressing the specific objectives of the study [164]. This can include a description of how the induction of the disease, disorder, or injury is sufficiently analogous to the human condition; how the model responds to known clinically effective treatments; how similar symptoms are to the clinical disease; and how animal characteristics were selected to represent the age, sex, and health status of the clinical population [14].

Item 13. Objectives

Clearly describe the research question, research objectives and, where appropriate, specific hypotheses being tested.

Explanation. Explaining the purpose of the study by describing the question(s) that the research addresses allows readers to determine whether the study is relevant to them. Readers can also assess the relevance of the model organism, procedures, outcomes measured, and analysis used.

Knowing whether a study is exploratory or hypothesis-testing is critical to its interpretation. A typical exploratory study may measure multiple outcomes and look for patterns in the data or relationships that can be used to generate hypotheses. It may also be a pilot study, which aims to inform the design or feasibility of larger subsequent experiments. Exploratory research helps researchers to design hypothesis-testing experiments by choosing what variables or outcome measures to focus on in subsequent studies.

Testing a specific hypothesis has implications for both the study design and the data analysis [16,167]. For example, an experiment designed to detect a hypothesised effect will likely need to be analysed with inferential statistics, and a statistical estimation of the sample size will need to be performed a priori (see Item 2. Sample size). Hypothesis-testing studies also have a predefined primary outcome measure, which is used to assess the evidence in support of the specific research question (see Item 6. Outcome measures).

In contrast, exploratory research investigates many possible effects and is likely to yield more false positive results because some will be positive by chance. Thus, results from well-designed hypothesis-testing studies provide stronger evidence than those from exploratory or descriptive studies. Independent replication and meta-analysis can further increase the confidence in conclusions.

Clearly outline the objective(s) of the study, including whether it is hypothesis-testing or exploratory, or if it includes research of both types. Hypothesis-testing studies may collect additional information for exploratory purposes; it is important to distinguish which hypotheses were prespecified and which originated after data inspection, especially when reporting unanticipated effects or outcomes that were not part of the original study design.

Item 14. Ethical statement

Provide the name of the ethical review committee or equivalent that has approved the use of animals in this study and any relevant licence or protocol numbers (if applicable). If ethical approval was not sought or granted, provide a justification.

Explanation. Authors are responsible for complying with regulations and guidelines relating to the use of animals for scientific purposes. This includes ensuring that they have the relevant approval for their study from an appropriate ethics committee and/or regulatory body before the work starts. The ethical statement provides editors, reviewers, and readers with assurance that studies have received this ethical oversight [12]. This also promotes transparency and understanding about the use of animals in research and fosters public trust.

Provide a clear statement explaining how the study conforms to appropriate regulations and guidelines. Include the name of the institution where the research was approved and the ethics committee who reviewed it (e.g., Institutional Animal Care and Use Committee [IACUC] in the United States or Animal Welfare and Ethical Review Body [AWERB] in the United Kingdom) and indicate protocol or project licence numbers so that the study can be identified. Also add any relevant accreditation, e.g., American Association for Accreditation of Laboratory Animal Care (AAALAC) [171] or Good Laboratory Practice (GLP).

If the research is not covered by any regulation and formal ethical approval is not required (e.g., a study using animal species not protected by regulations or law), demonstrate that international standards were complied with and cite the appropriate reference. In such cases, provide a clear statement explaining why the research is exempt from regulatory approval.

Item 15. Housing and husbandry

Provide details of housing and husbandry conditions, including any environmental enrichment.

Explanation. The environment determines the health and wellbeing of the animals, and every aspect of it can potentially affect their behavioural and physiological responses, thereby affecting research outcomes [175]. Different studies may be sensitive to different environmental factors, and particular aspects of the environment necessary to report may depend on the type of study [176]. Examples of housing and husbandry conditions known to affect animal welfare and research outcomes are listed in Table 2; consider reporting these elements and any other housing and husbandry conditions likely to influence the study outcomes.

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Table 2. Examples of information to consider when reporting housing and husbandry, and their effects on laboratory animals.

https://doi.org/10.1371/journal.pbio.3000411.t002

Environment, either deprived or enriched, can affect a wide range of physiological and behavioural responses [206]. Specific details to report include, but are not limited to, structural enrichment (e.g., elevated surfaces, dividers); resources for species-typical activities (e.g., nesting material, shelters, or gnawing sticks for rodents; plants or gravel for aquatic species); and toys or other tools used to stimulate exploration, exercise (e.g., running wheel), and novelty. If no environmental enrichment was provided, this should be clearly stated with justification. Similarly, scientific justification needs to be reported for withholding food and water [207] and for singly housing animals [208,209].

If space is an issue, relevant housing and husbandry details can be provided in the form of a link to the information in a public repository or as supplementary information.

Item 16. Animal care and monitoring

16a. Describe any interventions or steps taken in the experimental protocols to reduce pain, suffering, and distress.

Explanation. A safe and effective analgesic plan is critical to relieve pain, suffering, and distress. Untreated pain can affect the animals’ biology and add variability to the experiment; however, specific pain management procedures can also introduce variability, affecting experimental data [212,213]. Underreporting of welfare management procedures contributes to the perpetuation of noncompliant methodologies and insufficient or inappropriate use of analgesia [213] or other welfare measures. A thorough description of the procedures used to alleviate pain, suffering, and distress provides practical information for researchers to replicate the method.

Clearly describe pain management strategies, including

• specific analgesic
• administration method (e.g., formulation, route, dose, concentration, volume, frequency, timing, and equipment used)
• rationale for the choice (e.g., animal model, disease/pathology, procedure, mechanism of action, pharmacokinetics, personnel safety)
• protocol modifications to reduce pain, suffering, and distress (e.g., changes to the anaesthetic protocol, increased frequency of monitoring, procedural modifications, habituation, etc.)

If analgesics or other welfare measures, reasonably expected for the procedure performed, are not performed for experimental reasons, report the scientific justification [214].

16b. Report any expected or unexpected adverse events.

Explanation. Reporting adverse events allows other researchers to plan appropriate welfare assessments and minimise the risk of these events occurring in their own studies. If the experiment is testing the efficacy of a treatment, the occurrence of adverse events may alter the balance between treatment benefit and risk [34].

Report any adverse events that had a negative impact on the welfare of the animals in the study (e.g., cardiovascular and respiratory depression, central nervous system disturbance, hypothermia, reduction of food intake). Indicate whether they were expected or unexpected. If adverse events were not observed, or not recorded during the study, explicitly state this.

16c. Describe the humane endpoints established for the study, the signs that were monitored, and the frequency of monitoring. If the study did not set humane endpoints, state this.

Explanation. Humane endpoints are predetermined morphological, physiological, and/or behavioural signs that define the circumstances under which an animal will be removed from an experimental study. The use of humane endpoints can help minimise harm while allowing the scientific objectives to be achieved [219]. Report the humane endpoints that were established for the specific study, species, and strain. Include clear criteria of the clinical signs monitored [134] and clinical signs that led to euthanasia or other defined actions. Include details such as general welfare indicators (e.g., weight loss, reduced food intake, abnormal posture) and procedure-specific welfare indicators (e.g., tumour size in cancer studies [50], sensory-motor deficits in stroke studies [220]).

Report the timing and frequency of monitoring, taking into consideration the normal circadian rhythm of the animal and timing of scientific procedures, as well as any increase in the frequency of monitoring (e.g., postsurgery recovery, critical times during disease studies, or following the observation of an adverse event). Publishing score sheets of the clinical signs that were monitored [221] can help guide other researchers to develop clinically relevant welfare assessments, particularly for studies reporting novel procedures.

This information should be reported even if no animal reached any of the humane endpoints. If no humane endpoints were established for the study, explicitly state this.

Item 17. Interpretation/scientific implications

17a. Interpret the results, taking into account the study objectives and hypotheses, current theory, and other relevant studies in the literature.

Explanation. It is important to interpret the results of the study in the context of the study objectives (see Item 13. Objectives). For hypothesis-testing studies, interpretations should be restricted to the primary outcome (see Item 6. Outcome measures). Exploratory results derived from additional outcomes should not be described as conclusive, as they may be underpowered and less reliable.

Discuss the findings in the context of current theory, ideally with reference to a relevant systematic review, as individual studies do not provide a complete picture. If a systematic review is not available, take care to avoid selectively citing studies that corroborate the results or only those that report statistically significant findings [223].

When appropriate, describe any implications of the experimental methods or research findings for improving welfare standards or reducing the number of animals used in future studies (e.g., the use of a novel approach reduced the results’ variability, thus enabling the use of smaller group sizes without losing statistical power). This may not be the primary focus of the research, but reporting this information enables wider dissemination and uptake of refined techniques within the scientific community.

17b. Comment on the study limitations, including potential sources of bias, limitations of the animal model, and imprecision associated with the results.

Explanation. Discussing the limitations of the work is important to place the findings in context, interpret the validity of the results, and ascribe a credibility level to its conclusions [227]. Limitations are unavoidable in scientific research, and describing them is essential to share experience, guide best practice, and aid the design of future experiments [228].

Discuss the quality of evidence presented in the study and consider how appropriate the animal model is to the specific research question. A discussion on the rigour of the study design to isolate cause and effect (also known as internal validity [229]) should include whether potential risks of bias have been addressed [9] (see Item 2. Sample size, Item 3. Inclusion and exclusion criteria, Item 4. Randomisation, and Item 5. Blinding).

Item 18. Generalisability/translation

Comment on whether, and how, the findings of this study are likely to generalise to other species or experimental conditions, including any relevance to human biology (where appropriate).

Explanation. An important purpose of publishing research findings is to inform future research. In the context of animal studies, this might take the form of further in vivo research or another research domain (e.g., human clinical trial). Thoughtful consideration is warranted, as additional unnecessary animal studies are wasteful and unethical. Similarly, human clinical trials initiated based on insufficient or misleading animal research evidence increase research waste and negatively influence the risk-benefit balance for research participants [229,232].

Consider the type of study conducted to assess the implications of the findings. Well-designed hypothesis-testing studies provide more robust evidence than exploratory studies (see Item 13. Objectives). Findings from a novel, exploratory study may be used to inform future research in a broadly similar context. Alternatively, enough evidence may have accumulated in the literature to justify further research in another species or in humans. Discuss what (if any) further research may be required to allow generalisation or translation. Discuss and interpret the results in relation to current evidence and, in particular, whether similar [233] or otherwise supportive [234] findings have been reported by other groups. Discuss the range of circumstances in which the effect is observed and factors that may moderate that effect. Such factors could include, for example, the population (e.g., age, sex, strain, species), the intervention (e.g., different drugs of the same class), and the outcome measured (e.g., different approaches to assessing memory).

Item 19. Protocol registration

Provide a statement indicating whether a protocol (including the research question, key design features, and analysis plan) was prepared before the study, and if and where this protocol was registered.

Explanation. Akin to the approach taken for clinical trials, protocol registration has emerged as a mechanism that is likely to improve the transparency of animal research [232,237,238]. Registering a protocol before the start of the experiment enables researchers to demonstrate that the hypothesis, approach, and analysis were planned in advance and not shaped by data as they emerged; it enhances scientific rigour and protects the researcher against concerns about selective reporting of results [239,240]. A protocol should consist of (1) the question being addressed and the key features of the research that is proposed, such as the hypothesis being tested, the primary outcome measure (if applicable), and the statistical analysis plan; and (2) the laboratory procedures to be used to perform the planned experiment.

Protocols may be registered with different levels of completeness. For example, in the Registered Report format offered by an increasing number of journals, protocols undergo peer review, and if accepted, the journal commits to publishing the completed research regardless of the results obtained [237].

Other online resources include the Open Science Framework [241], which is suitable to deposit PHISPS (Population; Hypothesis; Intervention; Statistical Analysis Plan; Primary; Outcome Measure; Sample Size Calculation) protocols [242] and provide researchers with the flexibility to embargo the preregistration, keep it from public view until the research is published, and selectively share it with reviewers and editors. The EDA can also be used to generate a time-stamped PDF, which sets out key elements of the experimental design [19]. This can be used to demonstrate that the study conduct, analysis, and reporting were not unduly driven by emerging data. As a minimum, we recommend registering protocols containing all PHISPS components as outlined above.

Provide a statement indicating whether or not any protocol was prepared before the study, and if applicable, provide the time-stamped protocol or the location of its registration. When there have been deviations from the protocol, describe the rationale for these changes in the publication so that readers can take this into account when assessing the findings.

Item 20. Data access

Provide a statement describing if and where study data are available.

Explanation. A data-sharing statement describes how others can access the data on which the paper is based. Sharing adequately annotated data allows others to replicate data analyses so that results can be independently tested and verified. Data sharing allows the data to be repurposed and new datasets to be created by combining data from multiple studies (e.g., to be used in secondary analyses). This allows others to explore new topics and increases the impact of the study, potentially preventing unnecessary use of animals and providing more value for money. Access to raw data also facilitates text and automated data mining [246].

An increasing number of publishers and funding bodies require authors or grant holders to make their data publicly available [247]. Journal articles with accompanying data may be cited more frequently [248,249]. Datasets can also be independently cited in their own right, which provides additional credit for authors. This practice is gaining increasing recognition and acceptance [250].

When possible, make available all data that contribute to summary estimates or claims presented in the paper. Data should follow the FAIR guiding principles [251]; that is, data are findable, accessible (i.e., do not use outdated file types), interoperable (can be used on multiple platforms and with multiple software packages), and reusable (i.e., have adequate data descriptors).

Data can be made publicly available via a structured, specialised (domain-specific), open-access repository such as those maintained by the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) or European Bioinformatics Institute (EBI, https://www.ebi.ac.uk/). If such a repository is not available, data can be deposited in unstructured but publicly available repositories (e.g., Figshare [https://figshare.com/], Dryad [https://datadryad.org/], Zenodo [https://zenodo.org/], or Open Science Framework [https://osf.io/]). There are also search platforms to identify relevant repositories with rigorous standards, e.g., FairSharing (https://fairsharing.org/) and re3data (https://www.re3data.org/).

Item 21. Declaration of interests

21a. Declare any potential conflicts of interest, including financial and nonfinancial. If none exist, this should be stated.

Explanation. A competing or conflict of interest is anything that interferes with (or could be perceived as interfering with) the full and objective presentation, analysis, and interpretation of the research. Competing or conflicts of interest can be financial or nonfinancial, professional or personal. They can exist in institutions, in teams, or with individuals. Potential competing interests are considered in peer review, editorial, and publication decisions; the aim is to ensure transparency, and in most cases, a declaration of a conflict of interest does not obstruct the publication or review process.

Examples are provided in Box 7. If unsure, declare all potential conflicts, including both perceived and real conflicts of interest [254].

21b. List all funding sources (including grant identifier) and the role of the funder(s) in the design, analysis, and reporting of the study.

Explanation. The identification of funding sources allows the reader to assess any competing interests and any potential sources of bias. For example, bias, as indicated by a prevalence of more favourable outcomes, has been demonstrated for clinical research funded by industry compared with studies funded by other sources [257–259]. Evidence for preclinical research also indicates that funding sources may influence the interpretation of study outcomes [254,260].

Report the funding information including the financial supporting body(s) and any grant identifier(s). If the study was supported by several sources of funding, list them all, including internal grants. Specify the role of the funder in the design, analysis, reporting, and/or decision to publish. If the research did not receive specific funding but was performed as part of the employment of the authors, name the employer.