Specifications tableSubjectStrategy and ManagementSubject AreaKey drivers of Port CompetitivenessMore specific subject areaDry Bulk Port OperationType of dataTable, Graph and FigureHow data was acquiredThe data was collected from all of Malaysian Dry Bulk Terminal Ports using questionnaire distributed through email. From the primary sources, the data was digitised from corresponding archive.Data formatRaw, Filtered and analysed dataData collection parametersMachines, Conventional labor oriented (CLO), Trucking efficiency minutes, Stockpile LocationsDescription of data collectionThe data was collected from around of Malaysian ports at dry bulk terminal using an online questionnaire distributed to 18 Ports through email. The dispersion of respondents corresponds approximately to the structure of Malaysian maritime at the dry bulk terminal. The data provides full responses from the head of the port management. Each respond assessed the level of efficiency factors by percentage rate given from the total of 100%.Data source locationNo.PortRegionCoordinate1SandakanSabah5.8120° N, 118.0769° E2Kota Kinabalu5°58′60.00′\'N 116°4′0.00′\'E3Kudat6°52′60.00″N 116°50′60.00″E4Labuan5.2765° N, 115.2430° E5Bintulu3°16′0.00″N 113°4′0.00″E6Tawau4.2460° N, 117.8807° E7Lahad Datu5.0202° N, 118.3495° E8Tanjung ManisSarawak2.1575° N, 111.3391° E9Kuching1°33′13.76″N 110°20′7.00″E10Sarikei2°7′60.00″N 111°31′60.00″E11Sibu02°17′16″N 111°49′51″E12KuantanCentral3.9767° N, 103.4242° E13KemamanEast Coast4°24′58.48″N 103°15′18.02″E\
14JohorSouthern1.4438° N, 103.9064° E15PenangNorthern5.4098° N, 100.3679° E16Lumut4°13′0.01″N 100°37′0.01″E17North27.0442° N, 82.2359° W18WestportWestern2.9833° N, 101.4190° EData accessibility<https://data.mendeley.com/datasets/jxj6dt54w6/1>Related research articleRozar, N. M., Razik, M. A., & Sidik, M. H. M. (2018). The Factor Analysis of the Antecedents of Dry Bulk Terminal for Port Operation Improvement in Malaysia. International Journal of Engineering and Md. 10(6), 1801--1805.

Value of the data {#sec0001}
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•In dry bulk terminal, the data encapsulates a large number of Malaysian ports efficiency dataset.•The data offers insight for assessing Malaysian Ports efficiency in dry bulk terminal where it can be used to comprehend the other terminals of Malaysian ports (e.g. changes in coastal shipping services and port facilities) into regional economic change; in the long run, give broad geographical and temporal coverage of the data.•The data uncovers the variances of efficiency factors in dry bulk terminal ports and for port managers in order to build a long-term action strategy.

1. Data description {#sec0002}
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[Table 2](#tbl0002){ref-type="table"} shows the normality test from four different techniques, namely Kolmogorov-Smirnov and Shapiro-Wilk. The normality test was conducted from 10 variables as at [Table 1](#tbl0001){ref-type="table"}. The result demonstrated that the dataset of Machines (VA.1), Conventional labor oriented (VA.2), Trucking efficiency \< 15 min (VA.3), Trucking efficiency 15 -- 30 min (VA.4), Trucking efficiency \> 30 min VA.5). These are one of the facilities for Malaysians' port managers to achieve higher level of efficiency in the port operation and it was categorised of cargo handling technology and equipment, and port information technology. Thus, affected in port trade to take initiatives to expand port capacity for trucking efficiency \[[@bib0001]--[@bib0002]\].Table 1Summary of the variable\'s descriptions.Table 1SymbolDescriptionsVA. 1MachinesVA. 2Conventional labor orientedVA.3Trucking efficiency \< 15 minVA.4Trucking efficiency 15 -- 30 minVA.5Trucking efficiency \> 30 minVA.6Stockpile Locations \< 1kmVA.7Stockpile Locations 1 km -- 3kmVA.8Stockpile Locations 3 km -- 5kmVA.9Stockpile Locations 5 km -- 10kmVA.10Stockpile Locations \> 10km

While, at [Table 2](#tbl0002){ref-type="table"} shows the normality test for Stockpile Locations as at [Table 1](#tbl0001){ref-type="table"}**.** were consisted Stockpile Locations \< 1 km (VA.5), Stockpile Locations 1 km -- 3 km (VA.6) Stockpile Locations 3 km -- 5 km (VA.7), Stockpile Locations 5 km -- 10 km (VA.8), Stockpile Locations \> 10 km (VA.9) are normal. [Table 3](#tbl0003){ref-type="table"} and [Fig. 2](#fig0002){ref-type="fig"} show the variability of all variables, i.e. the minimum, maximum, interquartile, median, mean standard deviation, Variance, skewness and Kurtosis. [Figs. 1](#fig0001){ref-type="fig"} and [2](#fig0002){ref-type="fig"} show the normality test and histogram for each variable, respectively. The strategic location of a port significantly increases its efficiencies. From [Fig. 1](#fig0001){ref-type="fig"}, the mean value for 18 ports are mostly equivalent for all types of variables. However, Stockpile Locations 5 km -- 10 km (VA.10) consistently showed low value. The results were related with the position refers to of \"diversion distance\" concept where ships deviate from main trunk routes to the port. It was discussed by [@bib0003] said that the centrality of shipping routes is vital not only because it acts a port gateway but also as a hub for transhipment.Table 2Summary of the Case Processing Summary/ normality test.Table 2Tests of NormalityKolmogorov-Smirnov[a](#tb2fn2){ref-type="table-fn"}Shapiro-WilkVariablesDescriptionStatisticdfSig.StatisticdfSig.VA. 1Machines.12818.200[\*](#tb2fn1){ref-type="table-fn"}.90018.058VA. 2Conventional labor oriented.27118.001.77818.001VA.3Trucking efficiency \< 15 min.23718.009.93818.270VA.4Trucking efficiency 15 -- 30 min.28718.000.90318.066VA.5Trucking efficiency \> 30 min.26218.002.85818.011VA.6Stockpile Locations \< 1km.25118.004.82218.003VA.7Stockpile Locations 1 km -- 3km.32318.000.73718.000VA.8Stockpile Locations 3 km -- 5km.35818.000.71018.000VA.9Stockpile Locations 5 km -- 10km.21118.034.85518.010VA.10Stockpile Locations \> 10km.22218.019.81818.003[^1][^2]Table 3Descriptive analysis of Demographic factors in dry bulk terminal for port efficiency.Table 3StatisticStd. ErrorVA.1Mean524.722280.3805895% Confidence Interval for MeanLower Bound355.1340Upper Bound694.31045% Trimmed Mean494.6358Median475.0000Variance116,298.683Std. Deviation341.02593Minimum100.00Maximum1491.00Range1391.00Interquartile Range426.50Skewness1.341.536Kurtosis2.6811.038VA.2Mean19.72223.7151995% Confidence Interval for MeanLower Bound11.8839Upper Bound27.56065% Trimmed Mean18.0247Median17.5000Variance248.448Std. Deviation15.76222Minimum.00Maximum70.00Range70.00Interquartile Range12.50Skewness2.085.536Kurtosis5.6161.038VA.3Mean40.83335.3359495% Confidence Interval for MeanLower Bound29.5755Upper Bound52.09125% Trimmed Mean40.0926Median40.0000Variance512.500Std. Deviation22.63846Minimum5.00Maximum90.00Range85.00Interquartile Range32.50Skewness.378.536Kurtosis−0.0281.038VA.4Mean43.61115.1893295% Confidence Interval for MeanLower Bound32.6626Upper Bound54.55965% Trimmed Mean43.1790Median40.0000Variance484.722Std. Deviation22.01641Minimum5.00Maximum90.00Range85.00Interquartile Range25.00Skewness.698.536Kurtosis.1691.038VA.5Mean15.55561.9339195% Confidence Interval for MeanLower Bound11.4754Upper Bound19.63585% Trimmed Mean15.3395Median20.0000Variance67.320Std. Deviation8.20489Minimum5.00Maximum30.00Range25.00Interquartile Range11.25Skewness.160.536Kurtosis−0.9561.038VA.6Mean18.88893.0932095% Confidence Interval for MeanLower Bound12.3628Upper Bound25.41505% Trimmed Mean18.4877Median12.5000Variance172.222Std. Deviation13.12335Minimum5.00Maximum40.00Range35.00Interquartile Range25.00Skewness.316.536Kurtosis−1.6341.038VA.7Mean15.55563.2561995% Confidence Interval for MeanLower Bound8.6856Upper Bound22.42555% Trimmed Mean14.7840Median10.0000Variance190.850Std. Deviation13.81484Minimum5.00Maximum40.00Range35.00Interquartile Range25.00Skewness1.000.536Kurtosis−0.7091.038VA.8Mean13.05563.5732095% Confidence Interval for MeanLower Bound5.5168Upper Bound20.59445% Trimmed Mean11.1728Median7.5000Variance229.820Std. Deviation15.15982Minimum.00Maximum60.00Range60.00Interquartile Range10.00Skewness2.086.536Kurtosis4.6271.038VA.9Mean6.94441.1532195% Confidence Interval for MeanLower Bound4.5114Upper Bound9.37755% Trimmed Mean6.6049Median5.0000Variance23.938Std. Deviation4.89264Minimum.00Maximum20.00Range20.00Interquartile Range5.00Skewness.773.536Kurtosis1.7621.038VA.10Mean5.0000.9038895% Confidence Interval for MeanLower Bound3.0930Upper Bound6.90705% Trimmed Mean5.0000Median5.0000Variance14.706Std. Deviation3.83482Minimum.00Maximum10.00Range10.00Interquartile Range10.00Skewness.000.536Kurtosis−1.1901.038Fig. 1The normality test chart for port efficiency in dry bulk terminal.Fig 1Fig. 2Histogram analysis for port efficiency in dry bulk terminal.Fig 2

2. Experimental design, materials, and methods {#sec0003}
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In summary, our ports data includes 18 different places. These ports are appearing to be consistently important places for ocean shipping. Others appear in the data in different benchmark years, which indicates real changes in use and was similar with the concept of the study by [@bib0004], but in this data has also distinct recording practices at different times and between the sources. [Fig. 2](#fig0002){ref-type="fig"} shows the aggregate distribution of the number of appearances of each variables for all ports.

Appendix A. Supplementary data

Supplementary data to this article can be found online at <https://data.mendeley.com/datasets/jxj6dt54w6/1>

Declaration of Competing Interest
=================================

The authors declare that they have no known competing financial interests or personal relation-ships that could have appeared to influence the work reported in this paper.

Appendix. Supplementary materials {#sec0005}
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Image, application 1Image, application 2

The authors also want to gratitude the Faculty Maritime Studies, University Malaysia Terengganu (UMT), Faculty Entrepreneurship and Business, University Malaysia Kelantan (UMK) for all support and dedication given along the process.

Supplementary material associated with this article can be found, in the online version, at doi:[10.1016/j.dib.2020.105858](https://doi.org/10.1016/j.dib.2020.105858){#interref0003}.

[^1]: This is a lower bound of the true significance.

[^2]: Lilliefors Significance Correction.
