This dataset contains GTFS Static data backed-up daily from the Malaysia Government's official open data portal. https://developer.data.gov.my/realtime-api/gtfs-static

It provides snapshots for us to (if wanting) in the future to compare how services progress over time. Currently the script runs daily, and best effort will be done to ensure all provided data will be captured nicely & properly.

Due to the velocity that the Open Data project is progressing, I can't really keep up whether new endpoints are added. Hence if you see any changes, do tell me at "tungnan5636 at gmail dot com"

From the official portal: This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You can use the data available on our platform for free as it's part of our commitment to open data. We encourage users to explore, analyze, and utilize the data to drive innovation and insights.

In the third edition of the Open Data Barometer (ODB), Malaysia is placed 51st out of 92 countries and eighth among 12 countries in East Asia and the Pacific. Malaysia’s position in the ODB, just slightly above China and Vietnam, speaks to the fact that open data is fairly new to the country, which has been beleaguered recently with grand corruption cases involving key leaders in government. It was only a year ago that the chief secretary of state announced that the government of Malaysia would introduce open government principles into its bureaucracy, provide a set of open data guidelines to government agencies, and select national open data champions to pursue the proactive disclosure of information. As part of this initiative, the government has launched an open data portal which contains roughly 1000 datasets (as of May 2016).

Sarawak Imports By Country quantified monthly in RM ie: Malaysia, Peninsular, China, People's Republic Of, Japan, Indonesia, USA, Singapore, Republic Of, Korea, Republic Of, Thailand, Germany, Taiwan, Italy, Netherlands, United Kingdom and Others Data starts from Jan 2017 up to March 2021.

This dataset shows the Value added of manufacturing sector by state, 2005-2019 at constant pricesNotes:Supra State covers production activities that beyond the centre of predominant economic interest for any statee : Estimatep : Preliminary

This data set contains four ASCII data files (.txt format), one providing net primary production (NPP) component data and three providing climate data. The NPP studies were conducted in a lowland tropical rainforest in the Pasoh Forest Reserve, Malaysia (2.98 N 102.31 E) from 1971 through 1973. Precipitation and temperature data are available from weather stations located about 25 km from the study sites.The main part of the 2,450 ha Pasoh Forest Reserve is covered by lowland dipterocarp forest, with a core area of about 600 ha of undisturbed forest surrounded by a buffer zone of regenerating logged lowland forest. Annual rainfall ranges from 1,728 to 3,112 mm (mean 2,054 mm), which is relatively low for Malaysia, but the fairly even distribution of rain throughout the year permits the development of a typical lowland rainforest.Annual average litterfall data (1,055 g/m2/year) are available for several sub-sites based on bi-weekly collections. An additional 300 g/m2/year of leaf production was estimated to have been consumed by insects, and large wood fall/mortality was estimated to be 370 g/m2/year. Annual tree biomass increment was determined to be 640 g/m2/year, and a further 60 g/m2/year was allowed for root increments and 400 g/m2/year for root turnover. Including additional corrections to account for wood decay before measurement, total NPP was estimated to be 2,780 g/m2/year.

Sarawak Export by Country for several country i.e: Malaysia, Peninsular, Sabah, Japan, China, People's Republic Of, Korea, Republic Of, Taiwan, India, Thailand, Philippines, Singapore, Republic Of, USA and others.

Statistik pengunjung yang melayari Portal Kementerian Pertahanan Malaysia bagi tahun 2017

Estimate Population By Category Of Patients & Age Groups By State Malaysia, 2016

Quality checked and gap-filled monthly Landsat observations of surface reflectance at global eddy co-variance sites for the time period 1984-2022. Two product versions: one features all Landsat pixels within 2km radius around a given site, and a second version consists of an average time series that represents the area within 1km2 around a site. All data layers have a complementary layer with gap-fill information. Landsat data comprise all sites in the Fluxnet La Thuile, Berkeley and ICOS Drought 2018 data releases. Reflectance products: enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), generalized NDVI (kNDVI), near infra-red reflectance of vegetation (NIRv), normalized difference water index (NDWI) with both shortwave infra-red bands as reference, the scaled wide dynamic range vegetation index (sWDRVI), surface reflectance in individual Landsat bands. Based on the Landsat 4,5,7,8 collection 1 products with a pixel size of 30m. Supplementary data to Walther, S., Besnard, S., Nelson, J.A., El-Madany, T. S., Migliavacca, M., Weber, U., Ermida, S. L., Brümmer, C., Schrader, F., Prokushkin, A., Panov, A., Jung, M. , 2021. A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset, in preparation for Biogeosciences Discussions. ZIP archive of netcdf files for stations in Asia: ID-Pag, IL-Yat, JP-MBF, JP-Mas, JP-SMF, JP-Tak, JP-Tef, JP-Tom, KR-Hnm, KR-Kw1, MY-PSO, TW-Tar Besnard, S., Walther, S., Weber, U. (2023). The FluxnetEO dataset (Landsat) for Asian stations located in Indonesia, Israel, Japan, South Korea, Malaysia, and Taiwan, 1984-01-31–2022-12-30, Miscellaneous, https://hdl.handle.net/11676/VDa2N0DQMM2r6hv296F1Gd1h

Aggregated figures for natural hazard related events in EM-DAT: Malaysia

Documentation on the Country Profiles available here

How to cite the EM-DAT Project here

Main dataset on HDX: EM-DAT - Country Profiles

More on the EM-DAT database : website / data portal

Each line corresponds to a given combination of year, country, disaster subtype and reports figures for :

• number of disasters
• total number of people affected
• total number of deaths
• economic losses (original value and adjusted)

Sarawak Import By Country quantified monthly in RM ie: Malaysia, Peninsular, China, People's Republic Of, Japan, Indonesia, USA, Singapore, Republic Of, Korea, Republic Of, Thailand, Germany, Taiwan, Italy, Netherlands, United Kingdom and Others

This dataset contains historical data from WHO's data portal.

Malaysia Number of Motor Vehicle: Annual: Public: Others: Portal Rakan Niaga data was reported at 2,461.000 Unit in 2017. This records an increase from the previous number of 1,506.000 Unit for 2016. Malaysia Number of Motor Vehicle: Annual: Public: Others: Portal Rakan Niaga data is updated yearly, averaging 1,506.000 Unit from Dec 2014 (Median) to 2017, with 3 observations. The data reached an all-time high of 2,461.000 Unit in 2017 and a record low of 26.000 Unit in 2014. Malaysia Number of Motor Vehicle: Annual: Public: Others: Portal Rakan Niaga data remains active status in CEIC and is reported by Malaysian Automotive Association. The data is categorized under Global Database’s Malaysia – Table MY.TA004: Motor Vehicles Registration.

Data ini merupakan Jumlah Tenaga Kerja Indonesia (TKI) ke Negara Malaysia Menurut Bulan di Kabupaten Deli Serdang Tahun 2023.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

Follow data.kapsarc.org for timely data to advance energy economics research.

Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

This is version 2 of quality checked and gap-filled daily MODIS observations of surface reflectance and land surface temperature at global eddy co-variance sites for the time period 2000-2022. Two product versions: one features all MODIS pixels within 2km radius around a given site, and a second version consists of an average time series that represents the area within 1km2 around a site. All data layers have a complementary layer with gap-fill information. MODIS data comprise 647 eddy covariance sites (see a detailed list in the README of version 2). FluxnetEO v2 MODIS reflectance products: enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), generalized NDVI (kNDVI), near infra-red reflectance of vegetation (NIRv), normalized difference water index (NDWI) with band 5, 6, or 7 as reference, the scaled wide dynamic range vegetation index (sWDRVI), surface reflectance in MODIS bands 1-7. Based on the NASA MCD43A4 and MCD43A2 collection 6 products with a pixel size of 500m. FluxnetEO v2 MODIS land surface temperature: Terra and Aqua, day and night, at native viewing zenith angle as well as corrected to viewing zenith angles of 0 and 40degrees (Ermida et al., 2018, RS, https://www.mdpi.com/2072-4292/10/7/1114). Based on NASA MOD11A1 and MYD11A1 collection 6 at a pixel size of 1km. This is version 2 of the data described in Walther* & Besnard* et al. 2022. A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset, Biogeosciences, https://doi.org/10.5194/bg-19-2805-2022. Please refer to the README of version2 to understand the details of the updates between version1 (described in the paper) and version2. The data are separated in zip-files by continents and groups of countries. ZIP archive of netcdf files for stations in Asia: ID-Pag, IL-Yat, JP-MBF, JP-Mas, JP-SMF, JP-Tak, JP-Tef, JP-Tom, KR-Hnm, KR-Kw1, MY-PSO, TW-Tar Nelson, J., Walther, S., Weber, U. (2023). The FluxnetEO dataset (MODIS) for Asian stations located in Indonesia, Israel, Japan, South Korea, Malaysia, and Taiwan, 2000-01-01–2022-12-30, Miscellaneous, https://hdl.handle.net/11676/fD9dA8ScBA0rBEa09ArD_X29

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea)., 1.     INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limi..., , # Satellite images and road-reference data for AI-based road mapping in Equatorial Asia

https://doi.org/10.5061/dryad.bvq83bkg7

1. INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).  2. FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, cited below:  Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Remote Sensing. 16(5): 839. [https://doi.org/10.3390/rs16050839](https://doi.org/10.3...

Sarawak population by District, Administrative District, Ethnic and Age Group from 2010 - 2020p.Population Projections (Revised) based on the Population and Housing Census of Malaysia 2010.

Malaysia Number of Motor Vehicle: Public: Truck: Portal Rakan Niaga data was reported at 3,122.000 Unit in Jun 2017. This records an increase from the previous number of 2,840.000 Unit for Mar 2017. Malaysia Number of Motor Vehicle: Public: Truck: Portal Rakan Niaga data is updated quarterly, averaging 1,749.000 Unit from Dec 2014 (Median) to Jun 2017, with 7 observations. The data reached an all-time high of 3,122.000 Unit in Jun 2017 and a record low of 355.000 Unit in Dec 2014. Malaysia Number of Motor Vehicle: Public: Truck: Portal Rakan Niaga data remains active status in CEIC and is reported by Malaysian Automotive Association. The data is categorized under Global Database’s Malaysia – Table MY.TA004: Motor Vehicles Registration.

This dataset shows Malaysia’s imports by 5 digit SITC classification by country of origin.