presentation of experimental data and results

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Many believe that a scientist’s most difficult job is not conducting an experiment but presenting the results in an effective and coherent way. Even when your methods and technique are sound and your notes are comprehensive, writing a report can be a challenge because organizing and communicating scientific findings requires patience and a thorough grasp of certain conventions. Having a clear understanding of the typical goals and strategies for writing an effective lab report can make the process much less troubling.

General Considerations

It is useful to note that effective scientific writing serves the same purpose that your lab report should. Good scientific writing explains:

• The goal(s) of your experiment
• How you performed the experiment
• The results you obtained
• Why these results are important

While it’s unlikely that you’re going to win the Nobel Prize for your work in an undergraduate laboratory course, tailoring your writing strategies in imitation of professional journals is easier than you might think, since they all follow a consistent pattern. However, your instructor has the final say in determining how your report should be structured and what should appear in each section. Please use the following explanations only to supplement your given writing criteria, rather than thinking of them as an indication of how all lab reports must be written.

Analysis and presentation of experimental results : with examples, problems and programs

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• Basic statistical concepts.- Measurement errors.- A though experiment.- The statistical analysis of experimental results.- The presentation of numerical results.- The propagation of errors.- The three basic probability distributions.- The statistics of radioactivity.- Elements from the theory of errors.- Comparison and rejection of measurements.- The method of least squares.- Graphs.- The written report of the results of an experiment.- Appendix 1. Least squares straight line y = + x . The errors in and
• Appendix 2. Dimensional analysis.- Appendix 3. The use of random numbers in finding values of a variable x which are distributed according to a given probability density function f(x).- Appendix 4. Values of fundamental physical constants.- Answers to the problems.- List of programs and code samples.- Index.
• (source: Nielsen Book Data)
• Basic statistical concepts
• Measurement errors
• A though experiment
• The statistical analysis of experimental results
• The presentation of numerical results
• The propagation of errors
• The three basic probability distributions
• The statistics of radioactivity
• Elements from the theory of errors
• Comparison and rejection of measurements
• The method of least squares
• The written report of the results of an experiment
• Appendix 1. Least squares straight line y =α + λ x . The errors in α and λ
• Appendix 2. Dimensional analysis
• Appendix 3. The use of random numbers in finding values of a variable x which are distributed according to a given probability density function f(x)
• Appendix 4. Values of fundamental physical constants
• Answers to the problems
• List of programs and code samples

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• Experimental reporting requirements

Experimental details and characterisation required for journal articles

Guidance on reporting experimental procedures and compound characterisation

We believe that where possible, all data associated with the research in a manuscript should be Findable, Accessible, Interoperable and Reusable (FAIR), enabling other researchers to replicate and build on that research. We strongly encourage authors to deposit the data underpinning their research in appropriate repositories and make it as openly accessible as possible.

For all submissions to our journals, any data required to understand and verify the research in an article must be made available on submission. To comply, we suggest authors deposit their data in an appropriate repository. Where this isn’t possible, we ask authors to include the data as part of the article Supplementary Information. If necessary data are not made available, authors may be requested to provide these as part of the peer-review process, or in light of any post-publication concerns.

Please see our Data sharing guidance and policy for more details on specific data types and recommended repositories.

Some journals may also have additional subject requirements for both sharing and/or publishing supporting data, so please ensure you check the journal-specific guidelines.

On this page

General guidance, presentation of experimental data, post-acquisition processing of data.

• Data citation

Human and animal welfare

Biomolecules, characterisation of compounds and materials, computational studies and modelling, electrophoretic gels and blots, fluorescence sensors, inorganic and organometallic compounds, macromolecular structure and sequence data.

• Magnetic measurements 

Nanomaterials

Organic compounds, polymers and macromolecules, synthetic procedures, system models, x-ray crystallography, view all guidelines for preparing and formatting your article.

Please note, these guidelines are relevant to all of our journals. Make sure that you check your chosen journal’s web pages for specific guidelines too.

These experimental reporting requirements apply to both new compounds and known compounds prepared by a new or modified method.

It is the authors’ responsibility to provide descriptions of the experiments in enough detail to enable other skilled researchers to accurately reproduce the work.

Experimental procedures, compound characterization data, research materials necessary to enable the reproduction of an experiment and references to the associated literature should be provided in the experimental section of the manuscript.

Standard techniques and methods used throughout the work should be stated at the beginning of the experimental section; descriptions of these are not needed.

For known compounds synthesised via a literature procedure, authors should provide a reference to previously published characterization data.

Sources of starting materials obtained need not be identified unless the compound is not widely available, or the source is critical for the experimental result. Only non-standard apparatus should be described and commercially available instruments can be referred to by their stock numbers.

The accuracy of primary measurements should be stated. Figures should include error bars where appropriate, and results should be accompanied by an analysis of experimental uncertainty. Care should be taken to report the correct number of significant figures throughout the manuscript.

Any unusual hazards associated with the chemicals, procedures or equipment should be clearly identified.

For studies that involve the use of live animals or human subjects please refer to our Human and Animal Welfare policy .

Please see the sections below for detailed information about how to present specific types of data.

Data associated with particular compounds should be listed after the name of the compound concerned, following the description of its preparation. If comparison is to be made with literature values, these should be quoted in parentheses - for example, mp 157 °C (from chloroform) (lit., 19 156 °C), or ν max /cm -1 2020 and 1592 (lit., 24 2015 and 1600).

The suggested order in which the most commonly encountered data for a new compound should be cited follows:

Melting point

Optical rotation, refractive index, elemental analysis, uv absorptions, ir absorptions.

• NMR spectrum
• Mass spectrum

You can find more information about each of these below:

The following information is a guide to the presentation of experimental data, including appropriate formats for citation.

Yield should be presented in parentheses after the compound name (or its equivalent). Weight and percentage should be separated by a comma – for example, the lactone (7.1 g, 56%).

The melting point should be presented in the form mp 75 °C (from EtOH) - that is, the crystallisation solvent in parentheses. If an identical mixed melting point is to be recorded, the form mp and mixed mp 75 °C is appropriate.

The units should be stated in the preamble to the Experimental section – for example, [ α ] D values are given in 10 −1 deg cm 2 g −1 . This should be shown in the form [α] D 22–22.5 (c 0.95 in EtOH) – that is, concentration and solvent in parentheses.

Given in the form n D 22 1.653.

For the presentation of elemental analyses, both forms (Found: C, 63.1; H, 5.4. C 13 H 13 NO 4 requires C, 63.2; H, 5.3%) and (Found: C, 62.95; H, 5.4. Calc. for C 13 H 13 NO 4 : C, 63.2; H, 5.3%) are acceptable. Analyses are normally quoted to the nearest 0.1%, but a 5 in the second place of decimals is retained.

If a molecular weight is to be included, the appropriate form is: [Found: C, 63.1; H, 5.4%; M (mass spectrum), 352 (or simply M+, 352). C 13 H 13 NO 4 requires C, 63.2; H, 5.3%; M, 352].

We encourage authors to provide instrumental details and the chromatograms of the performed measurements in the Supplementary Information where possible.

These should be given in the form λmax(EtOH)/nm 228 (ε/dm 3  mol -1  cm -1  40 900), 262 (19 200) and 302 (11 500). Inflections and shoulders are specified as 228infl or 262sh. Alternatively the following form may be used: λmax (EtOH)/nm 228, 262 and 302 (ε/dm 3  mol -1  cm -1  40 900, 19 200 and 11 500); log ε may be quoted instead of ε.

IR absorption should be presented as follows: ν max /cm -1  3460 and 3330 (NH), 2200 (conj. CN), 1650 (CO) and 1620 (CN). The type of signal (s, w, vs, br) can be indicated by appended letters (for example 1760vs).

For all NMR spectra δ values should be used, with the nucleus indicated by subscript if necessary (for example, δ H , δ C ). A statement specifying the units of the coupling constants should be given in the preamble to the Experimental section – for example,  J  values are given in Hz. Instrument frequency, solvent, and standard should be specified. For example: δ H (100 MHz; CDCl3; Me4Si) 2.3 (3 H, s, Me), 2.5 (3 H, s, COMe), 3.16 (3 H, s, NMe) and 7.3–7.6 (5 H, m, Ph).

A broad signal may be denoted by br, such as 2.43 (1 H, br s, NH). Order of citation in parentheses: (i) number of equivalent nuclei (by integration), (ii) multiplicity (s, d, t, q), (iii) coupling constant – for example,  J 1,2  2,  J A B 4, (iv) assignment; italicisation can be used to specify the nuclei concerned (for example, CH3CH2). The proton attached to C-6 may be designated C(6)H or 6-H; the methyl attached to C-6, 6-Me or C(6)Me.

Mutually coupled protons in  1 H NMR spectra should be quoted with precisely matching J values, in order to assist thorough interpretation. In instances of any ambiguities when taking readings from computer printouts, mean  J  values should be quoted, rounded to the nearest decimal point.

Mass spectrometry data

Mass spectrometry data should be given in the form:  m/z  183 (M + , 41%), 168 (38), 154 (9), 138 (31) etc. The molecular ion may be specified as shown if desired. Relative intensities should be shown in parentheses (% only included once). Other assignments may be included in the form  m/z  152 (33, M − CH 3 CONH 2 ). Metastable peaks may be listed as: M* 160 (189→174), 147 (176→161), etc. The type of spectrum (field desorption, electron impact, etc.) should be indicated. Exact masses quoted for identification purposes should be accurate to within 5 ppm (EI and CI) or 10 ppm (FAB or LSIMS).

Authors might be asked during peer review to provide the original unprocessed data to the editors/reviewers of the journal.

All image acquisition and processing tools (including their settings) should be clearly stated in the manuscript. The amount of post-acquisition processing of data should be kept to a minimum. Any type of alteration such as image processing, cropping and groupings should be clearly stated in the figure caption and the Supplementary Information (SI) - clearly describing the process of alteration. Data manipulation (for example, normalisation or handling of missing values) should be noted.

Image processing changes should be applied to the entire image as well as all other images it is compared to. Processed images should still represent all the original data (with no data missing) and touch-up tools should be avoided.

Genuine and relevant signals in spectra should not be lost due to image enhancement.

Microscopy images of cells from multiple fields should not be compared but shown as single images (at least as part of the deposited data or in the SI).

Data Citation

For author-generated datasets that are directly associated with the article, we encourage authors to add data citations as bibliographic references within the article and the Data Availability Statement (DAS). Within the DAS, the citation should be given alongside information on datasets associated with the study and where to find them.

For datasets associated with previous studies, we encourage authors to add data citations as bibliographic references within the main text as they are mentioned. Data citation is encouraged as an alternative to informal references or mentions of local identifiers.

Suggested reference format for data citations:

[A. Name, B. Name and C. Name], [Name of repository / type of dataset], [Deposition number], [Year], [DOI, or URL if not available, of the dataset].

An example:

P. Cui, D. P. McMahon, P. R. Spackman, B. M. Alston, M. A. Little, G. M. Day and A. I. Cooper, 2019, CCDC Experimental Crystal Structure Determination: 1915306, DOI: 10.5517/ccdc.csd.cc22912j

Please also refer to the guidelines from the relevant repository on which information to provide in a citation.

When a study involves the use of live animal subjects, authors should adhere to the Animal Research: Reporting In Vivo Experiments (ARRIVE) 2.0 guidelines. When a study involves the use of human subjects, authors should adhere to the general principles set out in the Declaration of Helsinki .

Authors must include in the "methods/experimental" section of the manuscript a statement that all experiments were performed in compliance with the relevant guidelines. The statement must name the institutional/local ethics committee that has approved the study, and where possible the approval or case number should be provided.

Details of all guidelines followed should be provided. A statement regarding informed consent is required for all studies involving human subjects. Reviewers may be asked to comment specifically on any cases in which concerns arise.

For studies involving the use of animal subjects, authors are encouraged to make the completed ARRIVE 2.0 checklist available during peer review, for example by sharing it as part of the Supplementary Information (SI) or citing the deposited item.

The journals’ editorial teams reserve the right to request additional information in relation to experiments on vertebrates or higher invertebrates as necessary for the evaluation of the manuscript e.g., in the context of appropriate animal welfare or studies that involve death as an experimental endpoint.

Authors and referees should note the following guidelines for articles reporting electrochemical data and setup of batteries. It is the authors’ responsibility to ensure that the following information is provided in the main manuscript or Supplementary Information as appropriate.

The setup used for electrochemical testing should be clearly specified in the Experimental Information. For example, full or half cells, reference electrode (if used), testing temperature, etc.

When reporting electrochemical performance data, the authors should clearly state how many experimental runs these data are based on. The electrochemical performance value calculations should be clearly explained (including information on using charging or discharging values). All electrochemical data should be reported to an appropriate number of significant figures, along with standard deviation and error bars on graphs.

When reporting electrode performance values , the thickness of the electrode and the mass percentage of all electrode components (active material, additive, binder, etc.), the total mass of the electrode, and the geometric area of the electrode should be provided.

When reporting device-level performance values, the mass percentage of all battery components (active material, additive, binder, casing, current collector, electrolyte, separator, etc.), the total mass of the battery, and the geometric area of the electrode should be reported.

The mass percent and theoretical capacity of the active material should be provided if the theoretical capacity of the studied material is known. The theoretical capacity should be used to calculate C-rate. Alternatively, a rigorous use of A g -1 is recommended.

Pre-cycling and/or first cycle data should be reported.

Calculations of battery capacity should report the capacity obtained (in mAh g -1 ; if appropriate, volumetric values can be added in the unit of mAh cm -3 ) with the cycling rate and at what cycle number this capacity was obtained clearly stated. Average capacities for ≥3 cells with standard deviation are preferred.

It is the authors’ responsibility to provide rigorous evidence for the identity and purity of the biomolecules (for example, enzymes, proteins, DNA/RNA, oligosaccharides, oligonucleotides) described.

The identity of the biomolecule should be substantiated by employing at least one appropriate method, which may include one or more of the following:

Mass spectrometry

• Sequencing data (for proteins and oligonucleotides)
• High field 1 H or 13 C NMR

The purity should be established by one or more of the following

• Gel electrophoresis
• Capillary electrophoresis

Sequence verification should also be carried out for nucleic acids in molecular biology, including all mutants; for new protein or gene sequences, the entire sequence should be provided. For organic synthesis involving DNA, RNA oligonucleotides, their derivatives or mimics, purity should be established using HPLC and mass spectrometry as a minimum.

Provide usual organic chemistry analytical requirements for the novel monomer ( see Organic compounds ). However, it is not necessary to provide this level of characterisation for the oligonucleotide into which the novel monomer is incorporated.

Provide sufficient detail to identify the species being used. Specific information on antibodies is essential. Commercial sources and, if new antibodies are generated, full experimental details such as immunogen/phage, species, protocols for mAb-) should be given. We strongly recommend authors use unique Resource Identifiers for model organisms, antibodies, and tools, and publish them with full descriptions.

Present scatter plots of data, sensitivity, and specificity values with confidence intervals and results of receiver operating characteristic curve analysis. If a marker is already routinely used for that disease, comparison with that marker should be included.

Where the screening of new catalysts is reported, authors should provide a mass balance for all reactions (using, for example, an internal standard in their analysis technique). Recycling efficiencies should be based on reaction rates measurements and not product yield as a function of cycle. It is highly desirable to report the reaction rate for the catalysts as turnover frequency or mass-specific activity or, for heterogeneous catalysts, as surface-specific activity.

It is the responsibility of authors to provide fully convincing evidence for the homogeneity and identity of all compounds whose preparations they describe. Evidence of both purity and identity is required to establish that the properties and constants reported are those of a compound as claimed.

Reviewers will assess the evidence in support of the homogeneity and structure of all new compounds. No hard and fast rules can be laid down to cover all types of compound, but evidence for the unequivocal identification of new compounds should, wherever possible, include good elemental analytical data – an accurate mass measurement of a molecular ion does not provide evidence of purity of a compound and should be accompanied by independent evidence of homogeneity.

Where elemental analytical data cannot be obtained, appropriate evidence that is convincing to an expert in the field may be acceptable. Normally, for diamagnetic compounds this entails, at a minimum, a high resolution mass spectrometry measurement along with assigned 1 H and/or 13 C NMR spectra devoid of visible impurities.

Spectroscopic information necessary for the assignment of structure should be given. How complete this information should be depends upon the circumstances; the structure of a compound obtained from an unusual reaction or isolated from a natural source should be supported by stronger evidence than one that was produced by a standard reaction from a precursor of undisputed structure.

Particular care should be taken in supporting the assignments of stereochemistry (both relative and absolute) of chiral compounds reported, for example by one of the following:

• NMR spectroscopy
• Polarimetry
• Correlation with known compounds of undisputed configuration

In cases where mixtures of isomers are generated (for example, E-Z isomers, enantiomers, diastereoisomers), the constitution of the mixture should usually be established using appropriate analytical techniques (for example, NMR spectroscopy, GC, HPLC) and reported in an unambiguous fashion.

For an asymmetric reaction in which an enantiomeric mixture is prepared, the direct measurement of the enantiomer ratio expressed as the enantiomeric excess (ee) is recommended, and is preferred to less reliable polarimetry methods.

If a compound is new more detailed characterisation will be required. A compound is considered to be new if:

• it has not been prepared before
• it has been prepared before but not adequately purified
• it has been purified but not adequately characterised
• it has been assigned an erroneous composition previously
• it is a natural product isolated or synthesized for the first time

We encourage authors reporting various compounds or compound libraries to apply the FAIR principles and include a summary file of these compounds as part of the submission. This file should be deposited in an appropriate repository or be provided as part of the Supplementary Information, and should adhere to the following:

• format - CSV (*.csv), TSV (*.tsv) or SDF (*.sdf)
• for chemical structures - relevant headers including SMILES , InChI  and InChIKey
• for chemical names - Name and Synonym
• for other comments - such data, metadata, etc

These instructions are based on FAIR chemical structures in the Journal of Cheminformatics (E.L. Schymanski & E.E. Bolton, Journal of Cheminformatics , 2021, 13 , 50).

We recommend that authors follow the guidelines for the nomenclature of new radiolabelled compounds, as laid out in Consensus nomenclature rules for radiopharmaceutical chemistry - setting the record straight (C.H.H., G.A.D. et al., Nuclear Medicine and Biology , 2017, 55 , v – xi).

Authors should provide sufficient information to enable readers to reproduce any computational results. If software was used for calculations and is generally available, it should be properly cited in the references. References to the methods upon which the software is based should also be provided.

Equations, data, geometric parameters/coordinates, or other numerical parameters essential to the reproduction of the computational results (or adequate references when available in the open literature) should be provided. Authors who report the results of electronic structure calculations in relative energies should also include the absolute energies obtained directly from the computational output files. These may be deposited in an appropriate repository and cited, or provided in the Supplementary Information (SI).

We ask that the following information be provided where possible:

• As a minimum, papers reporting QM work should include the atomic coordinates, energies, and number of imaginary frequencies for all computed stationary points
• The level of theory used for computations should be mentioned in the text, and/or in the caption of the first figure that reports the results of those computations
• Where calculations are performed with density functional theory, the integration grid used for the calculations should be specified
• All relevant total energies (potential energies, enthalpies, Gibbs free energies, etc.) should be reported for each computed species. For transition states (TSs), the magnitude of the frequency of the imaginary vibrational mode may also be reported
• For intrinsic reaction coordinate (IRC) calculations, any shoulders in the IRC plots should be noted. If IRC calculations reveal that a TS corresponded to a reaction pathway different from that suggested by simple animation of its imaginary mode, then the IRC results (plots and details of reactants/products) should be deposited in an appropriate repository and cited
• Where computational work is included as part of a synthetic study, full details of any theoretical characterization (for example, computational NMR or VCD) of products and/or important synthetic intermediates should be documented

We also strongly encourage xyz, .mol2 or .pdb files for coordinates to be shared via deposition in an appropriate repository.

It is the responsibility of the authors to provide the raw data for all electrophoretic gel and blot data, ensuring sufficient evidence to support their conclusions.

All Western blot and other electrophoresis data should be supported by the underlying raw images. The image of the full gel and blot, uncropped and unprocessed, should be made available on submission. We suggest authors deposit their data in an appropriate repository. Where this isn’t possible, we ask authors to include the data as part of the Supplementary Information. All samples and controls used for a comparative analysis should be run on the same gel or blot.

When illustrating the result, any cropping or rearrangement of lanes within an image should be stated in the figure legend and with lane boundaries clearly delineated. Alterations should be kept to a minimum required for clarity.

Each image should be appropriately labelled, with the closest molecular mass markers and lanes labelled. All details should be visible; over or underexposed gels and blots are not acceptable. Authors should be able to provide raw data for all replicate experiments upon request.

Studies on fluorescence sensor systems should include titrations covering a full range of analyte concentration, from the absence of analyte to a stoichiometric excess, taking the following factors into account:

• If the analyte shows significant absorption at the excitation or emission frequencies corrections should be carried out for Inner Filter Effects (IFEs). (note: fluorescence probes where the response mechanism is based on the Inner Filter Effect are not suitable for publication in NJC)
• A plateau should be observed at high analyte concentrations for the intensity vs. concentration plot when the sensing mechanism is based on association
• Calculated Limits of Detection (LoD) should be supported by experimental data at similar concentrations
• The intensity vs. concentration relationship should be fitted using suitable software. The Benesi-Hildebrand linearization method for the determination of the association constant should not be used without extensive consideration of the limitations that arise from the method’s assumptions ( see Chemical Society Reviews Tutorial 10.1039/C0CS00062K for further details.)

Plots reporting the Stern-Volmer relationship (I°/I vs. concentration; the same should be valid for its reciprocal I/I°) should show an intercept of 1. Significant variation from this is not acceptable.

The Stern-Volmer relationship should be justified by reference to an appropriate quenching mechanism, e.g. dynamic quenching should show a linear relationship, while static quenching can present an upward curvature for relatively high association constants (see Chemical Society Reviews Tutorial 10.1039/D1CS00422K for further discussion)

The performance of all sensor systems should be compared to the current state-of-the-art sensors for the same analyte, with any differences in requirements (e.g. solvent system) clearly stated; a suitable (and justified) set of interferences should also be tested and discussed.

A new chemical substance (molecule or extended solid) should have a homogeneous composition and structure. Where the compound is molecular, authors should provide data to unequivocally establish its homogeneity, purity and identification as described above.

In general, this should include elemental analyses or a justification for the omission of this data.

This is particularly important for NMR silent paramagnetic compounds where NMR data tends to be less useful in establishing purity. In some instances an assigned 1 H NMR spectrum of a paramagnetic compound that is demonstrably devoid of impurities may be acceptable.

It may be possible to substitute elemental analyses with high-resolution mass spectrometric molecular weights. This is appropriate, for example, with trivial derivatives of thoroughly characterised substances or routine synthetic intermediates. In all cases, relevant spectroscopic data (NMR, IR, UV-vis, etc.) should be provided in tabulated form or as reproduced spectra. These may be deposited in an appropriate repository and cited, or provided in the Supplementary Information (SI). However, it should be noted that, in general, mass spectrometric and spectroscopic data do not constitute proof of purity, and, in the absence of elemental analyses, additional evidence of purity should be provided (melting points, PXRD data, etc.).

Where the compound is an extended solid, it is important to unequivocally establish the chemical structure and bulk composition. Single crystal X-ray diffraction does not determine the bulk structure. Reviewers will normally look to see evidence of bulk homogeneity. A fully indexed powder diffraction pattern that agrees with single crystal data may be used as evidence of a bulk homogeneous structure, and chemical analysis may be used to establish purity and homogeneous composition.

Detailed information on the reporting requirements for X-ray crystallography, including small molecule single crystal data and powder diffraction data is available in the section on X-ray crystallography.

Novel macromolecular structures and newly reported nucleic acid or protein sequences and microarray data should be deposited in appropriate repositories. It is the responsibility of the authors to provide relevant accession numbers prior to publication.

A Data Availability Statement with suitable links to the deposited data should be included. Please see our Data Sharing policy for more details. For high-throughput studies, we encourage authors to refer to Minimum Information Standards as determined and maintained by the relevant communities. For further details see:

• Minimum information standard - Wikipedia
• Minimum Information for Biological and Biomedical Investigations - FAIRsharing Information Resource

The following should be supplied for macromolecular X-ray structures:

• R merge , completeness, multiplicity and I /sigma(I) - both overall and in the outer resolution shell - for data, and
• R cryst , R free and the bond and angle deviations for coordinates
• a Ramachandran plot, and preferably
• real space R -factor

For NMR structures equivalent data plus resonance assignments should be supplied - number of restraints (NOEs and  J -couplings), RMS restraint deviation, etc, plus resonance assignments should be supplied.

All the above information should be included as summary data tables in the manuscript or may be deposited in an appropriate repository and cited, or provided in the Supplementary Information.

Magnetic measurements

If data from magnetic measurements are presented, the authors should provide a thorough description of the experimental details pertaining to how the sample was measured. If the data have been corrected for sample or sample holder diamagnetism, the diamagnetic correction term should be provided and the manner in which it was determined should be stated.

Any fit of magnetic data (for example, χ(T), χ(1/T), χT(T), μ(T), M(H), etc.) to an analytical expression should be accompanied by the Hamiltonian from which the analytical expression is derived, the analytical expression itself, and the fitting parameters. If the expression is lengthy, it may be deposited in an appropriate repository and cited, or relegated to the Supplementary Information to conserve space. When an exchange coupling constant (J) is quoted in the abstract, the form of the Hamiltonian should also be included in the abstract.

For nanomaterials (such as quantum dots, nanoparticles, nanotubes, nanowires), it is the authors’ responsibility not only to provide a detailed characterisation of individual components (see Inorganic and organometallic compounds ) but also a comprehensive characterisation of the bulk composition. Characterisation of the bulk sample could require determination of the chemical composition and size distribution over large portions of the sample.

All nanoparticulate materials should have been purified from synthesis by-products and residual parent compounds, ions etc. If they are to be applied in dispersed form (for example, as a nanoparticulate drug carrier), sufficient data on the dispersion state should be provided (for example, by dynamic light scattering, centrifugal analysis, nanoparticle tracking analysis).

SEM or TEM images for hybrid inorganic-organic nanoparticles should be provided in at least three different levels of magnification. Bar scales should be clearly visible. Images may be deposited in an appropriate repository and cited, or provided in the Supplementary Information (SI).

It is the responsibility of the authors to provide unequivocal support for the purity and assigned structure of all compounds using a combination of the following characterisation techniques.

Elemental analysis is recommended to confirm sample purity and corroborate isomeric purity. We encourage authors to provide instrumental details and the chromatograms of the performed measurements in the Supplementary Information where possible. Authors are also requested to provide 1 H, 13 C NMR spectra and/or GC/HPLC traces if satisfactory elemental analysis cannot be obtained.

For libraries of compounds, HPLC traces should be submitted as proof of purity. The determination of enantiomeric excess of nonracemic, chiral substances should be supported with either GC/HPLC traces with retention times for both enantiomers and separation conditions (that is, chiral support, solvent and flow rate) or for Mosher Ester/Chiral Shift Reagent analysis, copies of the spectra.

Important physical properties, for example, boiling or melting point, specific rotation, refractive index, including conditions and a comparison to the literature for known compounds, should be provided. For crystalline compounds, the method used for recrystallisation should also be documented (that is, solvent etc.).

Mass spectra and a complete numerical listing of 1 H, 13 C NMR peaks in support of the assigned structure, including relevant 2D NMR and related experiments (that is, NOE, etc.) are required. As noted in Analytical , authors are requested to provide copies of these spectra. Infrared spectra that support functional group modifications, including other diagnostic assignments, should be included. High-resolution mass spectra are acceptable as proof of the molecular weight providing the purity of the sample has been accurately determined as outlined above.

For all soluble polymers, an estimation of molecular weight should be provided by size exclusion chromatography, including details of columns, eluents and calibration standards, intrinsic viscosity, MALDI TOF, etc. In addition, full NMR characterisation ( 1 H, 13 C) as for organic compound characterisation above.

For Gel Permeation Chromatography, molecular weight (Mw), molecular number (Mn) polydispersity index (PDI), and internal standards used should be specified, and associated images/spectra should be made available on submission. We suggest authors deposit their data in an appropriate repository. Where this isn’t possible, we ask authors to include the data as part of the article Supplementary Information (SI).

These should be described in enough detail so that a skilled researcher is able to repeat them. They should include the specific reagents, products and solvents with all of their amounts (g, mmol, for products: %), as well as clearly stating how the percentage yields are calculated.

• Reaction times, temperature and solvent quantities should be reported
• Reactions requiring heating - provide the heat source
• Reactions conducted using microwave heating - information on the type of vessel used and how the temperature was monitored should be given as well as the temperature reached or maintained
• Light-promoted reactions - report the light source and specific conditions
• Describe purification methods in detail
• GC or HPLC traces should be supported with retention times and separation conditions (support, solvent and flow rate)
• Centrifugation  - includes rotation speed, centrifugation/dialysis time, solution for resuspension, resuspension time and procedure for each centrifugation step

Synthetic procedures should also include all the characterisation data for the prepared compound or material. For a series of related compounds at least one representative procedure that outlines a specific example that is described in the text or in a table and that is representative of the other cases should be provided. For a multistep synthesis, spectra of key compounds and the final product should be included.

Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable to metabolic networks, cell-signalling pathways, regulatory networks, and many others.

We encourage authors to prepare models of biochemical reaction networks using SBML and to deposit the model in an open database such as the BioModels database  or  MetabolicAtlas .

Include some physical or experimental validation. Studies that screen a molecule against a set of receptors with no link to physical or experimental data are not suitable for publication.

These guidelines provide details for the presentation of single crystal and powder diffraction data; they apply to submissions to any of our journals.

Authors should present their crystal data in a CIF (Crystallographic Information File) format and deposit this with the  Cambridge Crystallographic Data Centre (CCDC) before submission. Data will be held in the CCDC's confidential archive until publication of the article, but it will be made accessible to reviewers and the publisher assigned to review the data.

At the point of publication, any deposited data will be made publicly available through the CCDC Access Structures service. In addition, organic and metal-organic structures will be curated into the Cambridge Structural Database, and inorganic structures will be curated into the Inorganic Crystal Structures Database (FIZ Karlsruhe).

Upon deposition, each data set is assigned a Digital Object Identifier (DOI), so that the crystal structure is unambiguously identified and registered.

Include CCDC or ICSD numbers in the manuscript prior to submission as part of a Data Availability Statement . During submission authors will be asked to cite CCDC or ICSD reference numbers; CIFs should not be submitted with the manuscript. Any revised CIFs should be deposited directly with the CCDC before the revised manuscript is submitted to us.

In addition, authors are required to provide a checkCIF report for their reported crystal data. The checkCIF report can be obtained via the  International Union of Crystallography's (IUCr) free checkCIF service , or as part of the CCDC deposition process. Any ‘level A' alerts in the report should be explained in the submission details for the article or an explanation provided within the submitted CIF. Authors should submit the checkCIF reports to the Royal Society of Chemistry along with the manuscript files.

If the editor deems it necessary during the peer-review process, the crystallography associated with the manuscript may undergo specialist crystallographic assessment, in which case a report will be provided along with the other reports from reviewers. Any points raised in this assessment should be attended to and all revised CIFs should be deposited with the CCDC prior to uploading the revised manuscript.

For recommended information to include in your CIF, please see the CCDC CIF Deposition Guidelines.  If SQUEEZE or MASK procedures are used, this should be noted in the CIF file.

We encourage authors to include hkl data in the deposited CIF file. Alternatively authors can submit hkl data and the structure files (.fcf) separately during deposition with the CCDC. Raw data accompanying a structure should be made available by the authors for the review process, on request.

Details of the data collection and CCDC numbers should be given in the Data Availability Statement.

For relevant structures that are published as CSD communications, and that have not appeared in the manuscript or a previous journal publication, details should be included in the Data Availability Statement and the appropriate DOI should be cited as a reference in the manuscript.

Where there is significant discussion about the crystallography, the description may be given in textual or tabular form, although the latter is more appropriate if several structure determinations are being reported in one paper. A table of selected bond lengths and angles, with estimated standard deviations, should be restricted to significant dimensions only. Average values may be given with a range of E.S.D.s for chemically equivalent groups or for similar bonds.

Procedures for data collection and structure analysis can be provided as part of the Supplementary Information. The following data are recommended:

• Chemical formula and formula weight ( M )
• Crystal system
• Unit cell dimensions (Å or pm, degrees) and volume, with estimated standard deviations, temperature
• Space group symbol (if non-standard setting give related standard setting)
• Number of formula units in unit cell ( Z )
• Number of reflections measured and/or number of independent reflections, R int
• Final R values (and whether quoted for all or observed data)
• Flack or Rogers parameter (if appropriate)

For example:

Single crystals of [Pd{C(CO 2 Me)[C(CO 2 Me)C(CO 2 Me)=

C(CO 2 Me)C(CO 2 Me)=C(CO 2 Me)]C 6 H 3 [CH(Me)NH 2 ]-2-NO 2 -5}Br] 4 were recrystallised from dichloromethane, mounted in inert oil and transferred to the cold gas stream of the diffractometer.

Crystal structure determination of complex 4:

Crystal data. C 28 H 31 BrCl 4 N 2 O 14 Pd, M = 947.66, orthorhombic, a = 11.096(1), b  = 17.197(2), c = 19.604(3) Å, U = 3741.0(9) Å 3 , T = 173 K, space group P2 1 2 1 2 1 (no.19), Z = 4, 6013 reflections measured, 5665 unique (R int = 0.031), which were used in all calculations. The final wR(F 2 ) was 0.099 (all data).

There may be cases where authors do not wish to include details or extensive discussion of a crystal structure determination. Examples include where only the connectivity has been established, data is marked as low quality at the CCDC, the structure is not integral to the conclusions of the article, or the structure has been discussed in a previous publication. Authors should be mindful of unnecessary fragmentation and the editor’s decision on this will be final.

Authors are encouraged to submit powder diffraction crystallographic data as a CIF (Crystallographic Information File) file to an appropriate repository. Please see the Data Sharing policy for further details. 

Authors should combine multiple data sets for a given manuscript into a single file. The individual structures in the combined file should be separated from each other by the sequence #===END at the beginning of a line.

Authors should identify the manuscript with which the electronic file is associated when they submit the file by entering the name of the manuscript at the top of the electronic file.

The information required for deposition includes the following:

• A table of final fractional atomic coordinates
• Any calculated coordinates (for example, hydrogen)
• A full list of bond lengths and angles with estimated standard deviations
• A full list of displacement parameters in the form B ij   or U ij   (in Å 2 or pm 2 )
• Full details of the refinement
• Profile difference plots for all analyses. Where a range of similar analyses are presented a minimum number of representative plots may be given

Unrefined powder diffraction data should normally be reported only if the data form part of the discussion presented in the paper, and should be restricted to new materials. In such cases, the following experimental details should be provided in either textual or tabular format:

• Diffractometer name and model
• Radiation wavelength (Å)
• Temperature of data collection
• Unit cell dimensions (Å or pm, degrees), if determined

Tables of 2 θ  data, or diagrams showing diffraction patterns of reaction products, should not normally be published in print unless they have some distinct feature of relevance that requires such detail to be present. In most cases, such data may be provided as Supplementary Information (SI).

For cases where the materials are new , but have similar powder data to other well-characterised materials, such data should not usually be included in the paper but can be deposited in an appropriate repository, with the relevant reference number included in a Data Availability Statement, and cited.

For refined powder diffraction data  (where atomic coordinates have been determined), if the procedures for data collection and structure analysis were routine, their description may be concise. When the analysis has not been of a routine nature, the authors should briefly detail the procedures used. In most cases, a table of atomic coordinates may be provided, which should give details of occupancies that are less than unity.

Anisotropic thermal parameters may be included if they form an important aspect of the study. Selected bond lengths and angles, with estimated standard deviations, should be given.

For Rietveld refinements, an observed + calculated + difference profile plot should normally be given for each structure determination, except where a significant number of similar refinements have been carried out. In such cases, only the minimum number of representative plots should be included in the article, with additional plots being deposited in an appropriate repository and cited, or included in the SI.

The following information should be provided:

• Unit cell dimensions (Å or pm, degrees)
• Space group
• Number of formula units in unit cell ( Z ) 
• Number of reflections
• Final R values ( R wp , R exp and R l ) and method of background treatment

Data should still be deposited with the CCDC and the appropriate CCDC reference numbers and DOIs cited.

For information on how to cite crystallographic data in your manuscript, please see the section on Data Citation.

Submit your journal article

Data sharing guidance and policy, author guidelines and information.

← Explore all information and guidelines for authors

Presenting Data – Graphs and Tables

Types of data.

There are different types of data that can be collected in an experiment. Typically, we try to design experiments that collect objective, quantitative data.

Objective  data is fact-based, measurable, and observable. This means that if two people made the same measurement with the same tool, they would get the same answer. The measurement is determined by the object that is being measured. The length of a worm measured with a ruler is an objective measurement. The observation that a chemical reaction in a test tube changed color is an objective measurement. Both of these are observable facts.

Subjective  data is based on opinions, points of view, or emotional judgment. Subjective data might give two different answers when collected by two different people. The measurement is determined by the subject who is doing the measuring. Surveying people about which of two chemicals smells worse is a subjective measurement. Grading the quality of a presentation is a subjective measurement. Rating your relative happiness on a scale of 1-5 is a subjective measurement. All of these depend on the person who is making the observation – someone else might make these measurements differently.

Quantitative  measurements gather numerical data. For example, measuring a worm as being 5cm in length is a quantitative measurement.

Qualitative  measurements describe a quality, rather than a numerical value. Saying that one worm is longer than another worm is a qualitative measurement.

	Quantitative	Qualitative
Objective	The chemical reaction has produced 5cm of bubbles.	The chemical reaction has produced a lot of bubbles.
Subjective	I give the amount of bubbles a score of 7 on a scale of 1-10.	I think the bubbles are pretty.

After you have collected data in an experiment, you need to figure out the best way to present that data in a meaningful way. Depending on the type of data, and the story that you are trying to tell using that data, you may present your data in different ways.

Data Tables

The easiest way to organize data is by putting it into a data table. In most data tables, the independent variable (the variable that you are testing or changing on purpose) will be in the column to the left and the dependent variable(s) will be across the top of the table.

Be sure to:

• Label each row and column so that the table can be interpreted
• Include the units that are being used
• Add a descriptive caption for the table

You are evaluating the effect of different types of fertilizers on plant growth. You plant 12 tomato plants and divide them into three groups, where each group contains four plants. To the first group, you do not add fertilizer and the plants are watered with plain water. The second and third groups are watered with two different brands of fertilizer. After three weeks, you measure the growth of each plant in centimeters and calculate the average growth for each type of fertilizer.

The effect of different brands of fertilizer on tomato plant growth over three weeks

Treatment	Plant Number
	1	2	3	4	Average
No treatment	10	12	8	9	9.75
Brand A	15	16	14	12	14.25
Brand B	22	25	21	27	23.75

Scientific Method Review:  Can you identify the key parts of the scientific method from this experiment?

• Independent variable – Type of treatment (brand of fertilizer)
• Dependent variable – plant growth in cm
• Control group(s) – Plants treated with no fertilizer
• Experimental group(s) – Plants treated with different brands of fertilizer

Graphing data

Graphs are used to display data because it is easier to see trends in the data when it is displayed visually compared to when it is displayed numerically in a table. Complicated data can often be displayed and interpreted more easily in a graph format than in a data table.

In a graph, the X-axis runs horizontally (side to side) and the Y-axis runs vertically (up and down). Typically, the independent variable will be shown on the X axis and the dependent variable will be shown on the Y axis (just like you learned in math class!).

Line graphs are the best type of graph to use when you are displaying a change in something over a continuous range. For example, you could use a line graph to display a change in temperature over time. Time is a continuous variable because it can have any value between two given measurements. It is measured along a continuum. Between 1 minute and 2 minutes are an infinite number of values, such as 1.1 minute or 1.93456 minutes.

Changes in several different samples can be shown on the same graph by using lines that differ in color, symbol, etc.

Bar graphs are used to compare measurements between different groups. Bar graphs should be used when your data is not continuous, but rather is divided into different categories. If you counted the number of birds of different species, each species of bird would be its own category. There is no value between “robin” and “eagle”, so this data is not continuous.

Scatter Plot

Scatter Plots are used to evaluate the relationship between two different continuous variables. These graphs compare changes in two different variables at once. For example, you could look at the relationship between height and weight. Both height and weight are continuous variables. You could not use a scatter plot to look at the relationship between number of children in a family and weight of each child because the number of children in a family is not a continuous variable: you can’t have 2.3 children in a family.

How to make a graph

• Identify your independent and dependent variables.
• Choose the correct type of graph by determining whether each variable is continuous or not.
• Determine the values that are going to go on the X and Y axis. If the values are continuous, they need to be evenly spaced based on the value.
• Label the X and Y axis, including units.
• Graph your data.
• Add a descriptive caption to your graph. Note that data tables are titled above the figure and graphs are captioned below the figure.

Let’s go back to the data from our fertilizer experiment and use it to make a graph. I’ve decided to graph only the average growth for the four plants because that is the most important piece of data. Including every single data point would make the graph very confusing.

• The independent variable is type of treatment and the dependent variable is plant growth (in cm).
• Type of treatment is not a continuous variable. There is no midpoint value between fertilizer brands (Brand A 1/2 doesn’t make sense). Plant growth is a continuous variable. It makes sense to sub-divide centimeters into smaller values. Since the independent variable is categorical and the dependent variable is continuous, this graph should be a bar graph.
• Plant growth (the dependent variable) should go on the Y axis and type of treatment (the independent variable) should go on the X axis.
• Notice that the values on the Y axis are continuous and evenly spaced. Each line represents an increase of 5cm.
• Notice that both the X and the Y axis have labels that include units (when required).
• Notice that the graph has a descriptive caption that allows the figure to stand alone without additional information given from the procedure: you know that this graph shows the average of the measurements taken from four tomato plants.

Descriptive captions

All figures that present data should stand alone – this means that you should be able to interpret the information contained in the figure without referring to anything else (such as the methods section of the paper). This means that all figures should have a descriptive caption that gives information about the independent and dependent variable. Another way to state this is that the caption should describe what you are testing and what you are measuring. A good starting point to developing a caption is “the effect of [the independent variable] on the [dependent variable].”

Here are some examples of good caption for figures:

• The effect of exercise on heart rate
• Growth rates of E. coli at different temperatures
• The relationship between heat shock time and transformation efficiency

Here are a few less effective captions:

• Heart rate and exercise
• Graph of E. coli temperature growth
• Table for experiment 1

Principles of Biology Copyright © 2017 by Lisa Bartee, Walter Shriner, and Catherine Creech is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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• Korean J Anesthesiol
• v.74(2); 2021 Apr

The principles of presenting statistical results: Table

Sang gyu kwak.

1 Department of Medical Statistics, Daegu Catholic University School of Medicine, Daegu, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Jong Hae Kim

3 Department of Anesthesiology and Pain Medicine, Daegu Catholic University School of Medicine, Daegu, Korea

Tae Kyun Kim

4 Department of Anesthesiology and Pain Medicine, Yangsan Hospital, Pusan National University School of Medicine, Busan, Korea

Dong Kyu Lee

5 Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, Seoul, Korea

Sangseok Lee

6 Department of Anesthesiology and Pain Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea

Jae Hong Park

7 Department of Anesthesiology and Pain Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea

Francis Sahngun Nahm

8 Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

9 Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea

General medical journals such as the Korean Journal of Anesthesiology (KJA) receive numerous manuscripts every year. However, reviewers have noticed that the tables presented in various manuscripts have great diversity in their appearance, resulting in difficulties in the review and publication process. It might be due to the lack of clear written instructions regarding reporting of statistical results for authors. Therefore, the present article aims to briefly outline reporting methods for several table types, which are commonly used to present statistical results. We hope this article will serve as a guideline for reviewers as well as for authors, who wish to submit a manuscript to the KJA.

Introduction

It has been encouraging to see the growing number of outstanding article submissions and publications in the Korean Journal of Anesthesiology (KJA) over the years. Unfortunately, however, the diversity of result presentation format, alongside the number of manuscripts, has resulted in confusion not only in the review and publication process but also in delivering appropriate information to the readers. Presenting results derived using similar statistical methods in a prescribed tabular format recommended by the journal will not only simplify the review and publication process but also help with readers’ understanding of the published content.

General methods of presenting easy-to-read results can be found in the previous article [ 1 ]. In this article, we present specific examples of the appropriate application of the Instruction for Authors of KJA 1) to the tabular results for various analysis methods commonly used in research.

Common statistical tests and tables

Various types of tables are used to clearly present various forms of research results. Even if presented independently, tables must contain the essential elements needed to convey the necessary information. For instance, the title must contain sufficient description of the content, while the body and footnotes of the table must describe in detail the statistical method and results of the analysis.

The following are the examples of typical tabular results commonly submitted to this journal. The data used in the examples were generated randomly, unless otherwise indicated, and do not reflect results of a specific study, i.e., the presented results have no clinical significance.

Common guidelines

Statistic provided in all the tables follow the guidelines on the representation of significant figures and statistics in the Instructions for authors provided by the KJA. There must be no blank spaces in the table and estimate, sample size (n), and the statistical method used must be appropriately included when presenting results using statistical analysis. Quantitative data can be expressed as a representative value and its distribution, such as ‘mean ± standard deviation’ or ‘median (first quartile, third quartile). Qualitative data can be expressed as ‘frequency (percent, %)’, et al. For statistical analyses involving variable transformation that changes the shape of the distribution (i.e., log transformation), statistic reflective of the original value should be used. An inverse statistic may also be expressed if needed. Description of the transformation method and transformed values must accompany variable transformation. For more information on data transformation, refer to Lee's article [ 2 ].

Based on the statistic derived from the sample data of the study, the population parameters are estimated. It is recommended to display a confidence interval (i.e., 95% CI) that is an interval estimator along with point estimators such as mean, median, proportion, coefficient, et al. The previously derived estimates are described together with the hypothesis test results. P value must be described in three decimal places, and a test statistic should be presented in detail so that statistical inference can be made. Presenting the effect size, if possible, can aid the interpretation of statistical results.

An explanation of the abbreviations must be included in the footnote even if an explanation is provided in the text so that the table can be interpreted independently. The unit of measure of each variable must be accurately described, and the number of samples should be presented in the title or alongside the variable.

One sample comparison

In one sample comparisons, the data of the experimental sample are compared to a specific reference value. The example in Table 1 is a comparison between the arterial pressures of the experimental sample with the reference value of 60 mmHg. Based on the distribution of experimental data, a parametric or non-parametric method of statistical analysis was applied, along with the difference between the reference value and that of the experimental sample and its 95% CI.

Example of One Sample Comparison with Reference Value

Variables	Results	Reference value	Difference (95% CI)	P value
MBP (mmHg, n = 30)	70.0 ± 5.0	60	10.0 (8.0, 12.0)	< 0.001
MBP (mmHg, n = 28)	70 (64.0, 75.0)	60	10.0 (8.0, 12.0)	< 0.001

Values are presented as mean ± SD or median (Q1, Q3). MBP: mean blood pressure.

* One-sample t-test,

† Two-sided P value < 0.05,

‡ Wilcoxon’s signed rank test. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point.

In the case of categorical data, proportions, etc. can be compared. One sample proportion test can be performed to compare the response rate with the reference value, and when the response rate is close to 0% or 100%, an exact binomial test is sometimes performed. The comparison results are described in Table 2 along with the 95% CI of the response rate.

Example of One Sample Test of Proportions

Variables	Positive response	Reference probability	Response rate (95% CI)	P value
PONV (n = 25)	9	0.20	0.36 (0.18, 0.57)	0.080
Itching sense (n=64)	5	0.02	0.08 (0.03, 0.17)	0.009

PONV: postoperative nausea and vomiting,

* One sample proportion test with continuity correction,

† Exact binomial test,

‡ Two-sided P value < 0.05. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point.

Comparison of two independent samples

Table 3 presents the results of comparing the mean arterial pressure and heart rate after endotracheal intubation when two types of endotracheal intubation devices were used. A parametric or non-parametric method of statistical analysis is applied depending on the distribution of the experimental data. The statistical method used is described in the table along with a representative value suitable for the distribution. To facilitate the interpretation of the results, the difference between the two groups is presented and the corresponding P value is presented to three decimal places.

Example of Independent Two Sample Comparison

Variables	Group S (n = 49)	Group P (n = 53)	Difference (95% CI)	P value
MBP (mmHg)	72.3 ± 14.3	73.1 ± 14.9	−0.8 (−6.5, 4.9)	0.781
Heart rate	89.0 (75.0, 103.0)	82.0 (72.0, 93.0)	7.0 (0, 14.0)	0.062

* Independent two sample t-test,

† Mann-Whitney U test. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point.

Comparison of matched pairs

Table 4 presents data from a study measuring mean blood pressure before and after the administration of a drug. The table presents results of administering the drug in a sample of hypertensive patients and a sample of patients with a body mass index over 30 kg/m 2 . The number of patients in each sample has been presented and the mean or median blood pressure was used as the representative value according to the distribution of measurements. A paired t-test was used to perform a paired comparison using the difference in pre- and post-treatment values for each patient. The statistically estimated differences are presented alongside the its 95% CI. The statistical method and P value is also clearly presented.

Example of Dependent Two Samples Comparison

Underlying factors	MBP		Mean difference (95% CI)	P value
	Pre-treatment	Post-treatment
Hypertension (n = 20)	74.0 ± 13.9	70.9 ± 13.6	3.1 (0.4, 5.8)	0.026
BMI > 30 kg/m (n = 25)	75.4 (66.8, 81.5)	73.9 (65.0, 84.5)	1.5 (-1.0, 4.0)	0.228

Values are presented as mean ± SD or median (Q1, Q3). BMI: body mass index, MBP: mean blood pressure.

* Paired t-test,

Comparison of three or more independent samples

Results from a study on pain control following a Cesarean section are presented in Table 5 [ 3 ]. The administered dose of morphine and time taken until the first dose was compared between a control group that received normal saline, a group that received intrathecal morphine and a group that received a quadratus lumborum block. Morphine requirement with a normal distribution was expressed as ‘mean ± standard deviation’, while the time taken until the first dose was expressed as a ‘median (Q1, Q3)’ value as it did not satisfy the normal distribution assumption. An accurate P value, up to 3 decimal places, and the number of samples in each group are presented, while a detailed description of the statistical method including the multiple comparison method for post hoc analysis is provided.

Example of Three Independent Samples Comparison

Variables	Control group (n = 30)	ITM group (n = 30)	QLB group (n = 30)	P value
Morphine requirement (mg)	61.0 ± 12.9	42.8 ± 10.4	18.2 ± 9.6	<0.001
Time to first morphine dose (h)	2 (0.5, 4)	8 (3, 24)	17 (6, 36)	0.002

Values are presented as mean ± SD or median (Q1, Q3). ITM: intrathecal morphine, QLB: quadratus lumborum block. P values indicate the statistical inference result of overall comparisons.

* One-way analysis of variance with Tukey’s method,

‡ Kruskal-Wallis H test with Dunn’s procedure. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point. Excerpt from Salama ER [ 3 ] results showing representative values and P value as examples of comparison of three independent samples.

Categorical data comparison

Table 6 presents results of an investigation analyzing the occurrence of successful endotracheal intubation, sore throat 1 hour following tracheal extubation, and post-surgical vocal cord paralysis in two groups treated with an existing versus newly developed endotracheal tube. Results were reported as the frequency of occurrence and relative frequency and the statistical method and P value are clearly presented.

Example of Categorical Data Comparison

Variables	Group N (n = 49)	Group C (n = 53)	P value
Successful tracheal intubation	44 (89.8)	32 (60.4)	0.001
Sore throat at 1 h	11 (22.4)	20 (37.8)	0.144
Vocal cord paralysis	1 (2.0)	2 (3.8)	>0.999

Values are presented as frequency (%).

* Chi-squared test,

‡ Fisher’s exact test. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point.

Logistic regression analysis

The dependent variable is a nominal scale. This analysis method is widely used when selecting a meaningful variable among various explanatory variables and results are presented in terms of odds ratio, etc. Parts of results of a study published in the KJA is presented in Table 7 [ 4 ]. The study analyzed the risk factors of post-anesthesia emergence agitation in the recovery room. Variables with three or more components were converted into insignificant dummy variables to estimate the odds ratio. The table presents the odds ratio of referenced components and those converted into dummy variables alongside the 95% CI. A detailed description of the statistical methods used to select variables in the logistic regression analysis is also included.

Risk Factors of Emergence Agitation in the PACU (n = 158) [ 4 ]

Variables	Odds ratio (95% CI)	P value
Marital status
Divorced	Reference
Single	0.16 (0.04, 0.64)	0.009
Married	0.16 (0.04, 0.62)	0.008
Pre-existing ND	6.78 (1.36, 33.80)	0.020
Gynecological surgery	0.29 (0.12, 0.71)	0.007
Thoracic surgery	0.23 (0.07, 0.80)	0.021
IO bleeding	1.00 (1.00, 1.00)	0.047
IO morphine administration	1.15 (1.03, 1.28)	0.015
Analgesic drugs in PACU	2.99 (1.56, 5.73)	0.001

The odds ratio of Marital status is estimated with non-weighted dummified variables. IO: intraoperative, ND: neurologic disorders, PACU: post-anesthesia care unit,

* Backward binary stepwise logistic regression,

† Two-sided P value < 0.05. These values, including P values, are presented according to the Instructions for Authors of Korean Journal of Anesthesiology for notation below the decimal point.

This article examined the principles of presenting the statistical results in clinical studies as a table. We hope to see manuscript submissions with standardized tables reflective of the provided framework. Such standardized format will help facilitate the submission and review process for both authors and reviewers.

1) https://ekja.org/authors/authors.php

Conflicts of Interest

All authors are Statistical Round Board Members in KJA.

Author Contributions

Sang Gyu Kwak (Conceptualization; Supervision; Writing – original draft; Writing – review & editing)

Hyun Kang (Validation; Writing – review & editing)

Jong Hae Kim (Validation; Writing – review & editing)

Tae Kyun Kim (Validation; Writing – review & editing)

EunJin Ahn (Validation; Writing – review & editing)

Dong Kyu Lee (Validation; Writing – original draft; Writing – review & editing)

Sangseok Lee (Project administration; Validation; Writing – review & editing)

Jae Hong Park (Validation; Writing – review & editing)

Francis Sahngun Nahm (Validation; Writing – review & editing)

Junyong In (Conceptualization; Supervision; Writing – original draft; Writing – review & editing)

This web page presents a sample laboratory report written in a thermal fluids course (ME 2984) at Virginia Tech. Accompanying this report is a that states what the instructors expected as far as the scope of the experiment and the depth and organization of the report.

where p is the pressure the gas, V is the volume, m is the mass, R is a constant, and T is temperature. This report presents the procedures for the experiment, the experiment's results, and an analysis of those results.









where V equals the voltage output (volts) from pressure transducer, and p equals the absolute pressure (kPa). Column four presents the measured values of temperature (K) calculated from the calibration curve for the thermocouple:

+ V/S where T equals the ice bath reference temperature (0°C), V equals the voltage (volts) measured across the thermocouple pair, and S equals the thermocouple constant, 42.4 µV/°C. Finally, column 5 presents the ideal values of temperature for the corresponding measured values of pressure. These ideal values arise from the ideal gas equation (PV=mrt). Figure A-1 shows the graph of temperature (K) versus pressure (kPa) for the measured case. Figure A-2 shows the graph of temperature versus pressure for the ideal case.

Data From Experiment

Voltage (V)	Voltage (V)	Pressure (kPa)	Temperature (K)	Temperature (K)
6.32	0.0011	99.90	298.94	312.17
6.39	0.0020	102.81	320.32	321.28
6.78	0.0031	119.82	346.26	374.44
7.31	0.0046	145.04	381.64	453.24
7.17	0.0052	138.14	395.79	431.69
7.35	0.0064	147.04	424.09	459.50
7.45	0.0073	152.11	445.32	475.32
7.56	0.0078	157.78	457.11	493.04
7.66	0.0097	163.02	501.92	509.43
8.06	0.0107	184.86	525.51	577.69
8.10	0.0114	187.12	542.02	584.75
8.34	0.0130	200.97	579.75	628.03

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Best practices for data management and sharing in experimental biomedical research

Affiliations.

• 1 Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal.
• 2 Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.
• 3 Meta-Research Innovation Center at Stanford (METRICS), Stanford, California, United States.
• 4 Department of Statistics, Stanford University, Stanford, California, United States.
• PMID: 38451234
• DOI: 10.1152/physrev.00043.2023

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

Keywords: biomedicine; data management; metadata; raw data; reporting guidelines; reproducibility.

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Experimental and theoretical study on anchorage loss of prestressed cfrp-reinforced concrete beams, 1. introduction, 2. fem calculation and theoretical analysis, 2.1. mechanism of anchorage loss, 2.2. description fem model, 2.3. analysis of fem results, 2.4. theoretical calculation analysis, 3. experimental study, 3.1. overview of project, 3.2. results and discussion, 3.3. theoretical validation, 4. conclusions.

• The FEM simulations revealed that the deformation of the connecting screw and the front end of the steel reaction frame accounts for approximately 95% of the total anchorage loss in a CFRP tendon–main beam connection system. The TCS was established based on the stress forms of these components, and the calculated results are consistent with the FEM analysis, indicating a high level of reliability.
• In the control group experiment, a CFRP tendon with a length of 12 m and a diameter of 12 mm, subjected to a prestress of 950 MPa, showed an anchorage loss of approximately 1.5% of the set prestress value, corresponding to an anchorage deformation of about 1 mm. Further studies indicated that the deformation magnitude is influenced by the prestress level, the dimensions of the steel reaction frame’s front end, the connecting screw length, and the thread gap values.
• The experimental results show relative discrepancies with both the FEM and theoretical results, attributed to gaps values ω 3 between the threaded connections. A 2 mm gap at each threaded connection was identified, and this can be reduced by tightening the threads with external force. However, the influencing pattern of these gaps requires further investigation. After adjusting the calculation formulas, the error was controlled within approximately 5%, demonstrating higher precision.
• This study provides a comprehensive framework for accurately predicting and mitigating anchorage loss in externally prestressed CFRP tendons, with significant implications for future engineering applications.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Component	Material	Elastic Modulus (MPa)	Poisson Ratio
Steel reaction frame	Q235	210,000	0.2
Fixed nut	Grade 8.8	210,000	0.3
Connecting screw	Grade 8.8	210,000	0.3
Anchor	Q235	210,000	0.3
CFRP tendon	CFRP	160,000	0.31

Component		Deformation (mm)	Proportion of Total Deformation (%)
Front end of the steel reaction frame		0.270822	41.09
Fixed nut		0.001575	0.24
Connecting screws	(a)	0.027201	54.87
	(b)	0.309988
	(c)	0.024496
Rear of anchor		0.025051	3.8
Total deformation		0.659133	/

Group	CFRP Tendon Length (mm)	Prestress (MPa)	Connecting Screw Length (mm)	Side Panel Spacing (mm)	Fixed Nut Torque (N·m)
1	12,000	950	139	79	/
2	12,000	950	135	59	400
3	12,000	950	137	59	/
4	12,000	475	374	59	400
5	12,000	475	370	59	/
6	12,000	950	374	59	400
7	12,000	950	376	59	/

Group	(MPa)	(MPa)	(MPa)	Deformation (a/mm)
1	948.51	933.88	14.63	1.061
2	951.52	941.65	9.87	0.716
3	947.98	935.71	12.27	0.890
4	486.86	476.28	10.58	0.784
5	485.58	472.43	13.16	0.954
6	975.28	957.20	18.08	1.311
7	962.36	941.14	21.22	1.539

Group	Steel Reaction Frame	Connection Screw	Deformation	(MPa)
1	0.267	0.364	0.631	8.70
2	0.112	0.355	0.467	9.196
3	0.111	0.359	0.470	11.995
4	0.057	0.471	0.528	10.046
5	0.057	0.465	0.522	12.719
6	0.118	0.978	1.097	17.888
7	0.113	0.936	1.049	19.988

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Liu, Q.; Jiang, H.; Tao, G.; Zhuge, P. Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams. Appl. Sci. 2024 , 14 , 6246. https://doi.org/10.3390/app14146246

Liu Q, Jiang H, Tao G, Zhuge P. Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams. Applied Sciences . 2024; 14(14):6246. https://doi.org/10.3390/app14146246

Liu, Qinrui, Haozhe Jiang, Guocheng Tao, and Ping Zhuge. 2024. "Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams" Applied Sciences 14, no. 14: 6246. https://doi.org/10.3390/app14146246

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Alumni Spotlight: Ruthe Huang, ScM '19

Ruthe Huang, ScM '19, is a Senior Data Scientist with Practical Intelligence, specializing in statistical modeling and data visualization.

Ruthe Huang, ScM '19, works for Practical Intelligence as a Senior Data Scientist, where she specializes in statistical modeling and data visualization. Her thesis, under the direction of Mei-Cheng Wang , was entitled "ADHD Risk Prediction in the Boston Birth Cohort: an Application of the Proportional Odds Model and ROC Curve Analysis.”

In her spare time, Huang volunteers for the Prison-to-Professionals program , tutoring math, statistics, and standardized test prep to criminal-justice involved groups. 2024 marks her seventh year working with the organization as their SAT/GRE Facilitator.

Describe your current position and responsibilities in a way that will inform current and prospective students about career opportunities in biostatistics.

I am a data scientist within the government contracting world, meaning that each client and problem set is never the same. For the last four years, I have been working for a Department of Defense client with uniquely frustrating data problems, including naïve and nontraditional sources of data, mission limitations to software and data access, and stakeholder distrust in statistical results. Much of my daily work involves not just data cleaning and statistical analysis but also working with software teams to deploy data products as well as presenting analytical results for a variety of audiences.

What sparked your interest in the field of biostatistics?

Early on in my undergraduate studies, I looked for a career field that would combine my two interests of public health and statistics; biostatistics was the perfect match. I loved that biostatistics was interdisciplinary, allowing me to master quantitative methods while still working in the realm of public health.

What led you to join the Johns Hopkins Department of Biostatistics?

I was biased in my decision, since I was a Hopkins undergrad and had the chance to experience how wonderful the faculty were before starting at Bloomberg. However, the ultimate decision came down to the small cohorts and robust statistical training that the Department offered.

I still find it challenging to convince stakeholders who are not familiar with data or quantitative methods to believe in the results of statistical modeling, but Dr. Zeger’s courses gave me the tools to continue improving my skills.

How did your degree prepare you for your career? What aspects of the Hopkins Biostatistics program did you find most useful?

The coursework of the ScM program took us through such rigorous detail that I left with the skillsets – and, more importantly, the confidence – to not fear strange or new data problems. In my career, I often face unorthodox problems with messy data; although I may not have learned how to deal with these problem sets through coursework, I have been able to apply foundational knowledge from our program to help clients conduct as robust of an analysis as possible or even help clients develop novel approaches.

In his Methods series, Dr. Zeger would often emphasize the importance of interpreting model results clearly and accurately. I think of this often in my career, as I find most of my presentations directed to (statistically) non-technical audiences. I still find it challenging to convince stakeholders who are not familiar with data or quantitative methods to believe in the results of statistical modeling, but Dr. Zeger’s courses gave me the tools to continue improving my skills.

What has been your most satisfying job experience using your biostatistics background?

It is often said that building data models is an art, not a science. I experienced this first-hand when I was able to identify a key result in data using only contextual knowledge and basic model-building techniques. A different organization analyzed the same dataset and used only machine learning algorithms. The other organization was not able to identify the key result from my simple classification model because they had not taken the time to understand the context from which the data came nor check the data assumption of using certain models.

How did you build your sense of community during your time as a Biostatistics student?

My cohort was the best part of my time as a Biostatistics student. Because we were a small group of six, we were able to spend a lot of time together both at Bloomberg as well as in our personal time. From working on assignments in our office to picking apples in Baltimore County, we were able to be a community of support for each other.

What was your favorite thing about living in Baltimore when you were a student?

My favorite thing about living in Baltimore as a student was exploring all the little pleasures the city has to offer. I always loved that Baltimore was a relatively small city, because it meant that there were less crowds to navigate through while enjoy the beautiful magnolia blossoms by the Washington Monument , eating the whole menu at Ekiben , or sweating your body weight at Artscape . Baltimore is beautiful in its juxtaposition: of loud and quiet, of dark and light, of chaotic and beautiful. MORE ALUMNI SPOTLIGHTS

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