The geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world. Currently 199 total entities are tracked, including all 195 UN member states, Greenland, Taiwan, Niue, and Kosovo. Comprehensive Global Administrative Zones (CGAZ) is a set of global composites for administrative boundaries. Disputed areas are removed and replaced with polygons following US Department of State definitions. It has three boundary levels ADM0, ADM1, and ADM2, clipped to international boundaries (US Department of State), with gaps filled between borders. This dataset is part of CGAZ. It was ingested from version 6.0.0 of Global Composite Files with DBF_DATE_LAST_UPDATE=2023-09-13. It shows boundaries at level ADM0 (country-level boundaries).
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2.
Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.
📈 Daily Historical Stock Price Data for ADM Hamburg Aktiengesellschaft (2000–2025)
A clean, ready-to-use dataset containing daily stock prices for ADM Hamburg Aktiengesellschaft from 2000-02-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.
🗂️ Dataset Overview
Company: ADM Hamburg Aktiengesellschaft Ticker Symbol: OEL.F Date Range: 2000-02-01 to 2025-05-28 Frequency: Daily Total… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-adm-hamburg-aktiengesellschaft-20002025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Description
This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by Warth et al. (2019) and Vetter-Gindele et al. (2019).
In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: adm) and a hexagon grid of 250 meter length (second dataset: hex).
The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017
Contents
Both datasets (adm and hex) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:
shp: polygon geometries (geometries of the administrative boundaries and hexagons)
dbf: attribute table (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)
shx: index file combining the geometries with the attributes
cpg: encoding of the attributes (UTF-8)
prj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for ArcGIS
qpj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for QGIS
lyr: symbology suggestion for the polygons(predefined is the number of local type shophouses in 2017) for ArcGIS
qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS
Citation and documentation
To cite this dataset, please refer to the publication
Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): Changes in the Building Stock of Da Nang between 2015 and 2017. Data, 5, 42. doi:10.3390/data5020042
This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.
Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4
You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/
Short introduction to the data
For the dataset, we collected, gap-filled, and harmonised:
a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and
sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).
These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).
We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.
Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.
List of datasets
Birth and death rates:
raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)
raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)
tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)
tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)
Net-migration:
raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)
raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)
raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)
raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)
polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)
polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)
polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)
Files to run online net migration explorer
masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.
Metadata
Grids:
Resolution: 5 arc-min (0.083333333 degrees)
Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)
Coordinate ref system: EPSG:4326 - WGS 84
Format: Multiband geotiff; each band for each year over 2000-2019
Units:
Birth and death rates: births/deaths per 1000 people per year
Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)
Geospatial polygon (gpkg) files:
Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)
Temporal extent: annual over 2000-2019
Coordinate ref system: EPSG:4326 - WGS 84
Format: gkpk
Units:
Net-migration: persons per 1000 people per year
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
This dataset displays level 0 world administrative boundaries. It contains countries as well as non-sovereign territories (like, for instance, French overseas).
CPEXAW-ADM-Aeolus_1 is the ESA ADM-Aeolus Datasets for the Convective Processes Experiment - Aerosols & Winds (CPEX-AW) sub-orbital campaign. Data collection for this product is complete.The Convective Processes Experiment – Aerosols & Winds (CPEX-AW) campaign was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. CPEX-AW is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in the summer of 2017. In addition to joint calibration/validation of ADM-AEOLUS, CPEX-AW studied the dynamics related to the Saharan Air Layer, African Easterly Waves and Jets, Tropical Easterly Jet, and deep convection in the InterTropical Convergence Zone (ITCZ). CPEX-AW science goals include:• Better understanding interactions of convective cloud systems and tropospheric winds as part of the joint NASA-ESA Aeolus Cal/Val effort over the tropical Atlantic;• Observing the vertical structure and variability of the marine boundary layer in relation to initiation and lifecycle of the convective cloud systems, convective processes (e.g., cold pools), and environmental conditions within and across the ITCZ;• Investigating how the African easterly waves and dry air and dust associated with Sahara Air Layer control the convectively suppressed and active periods of the ITCZ;• Investigating interactions of wind, aerosol, clouds, and precipitation and effects on long range dust transport and air quality over the western Atlantic.In order to successfully achieve the objectives of the campaign, NASA deployed its DC-8 aircraft equipped with an Airborne Third Generation Precipitation Radar (APR-3), Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. This campaign aims to provide useful material to atmospheric scientists, meteorologists, lidar experts, air quality experts, professors, and students. The Atmospheric Science Data Center (ASDC) archives the dropsonde, HALO, and DAWN data products for CPEX-AW. For additional datasets please visit the Global Hydrometeorology Resource Center (GHRC).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Acknowledgement and Disclaimers
These data are a product of a research activity conducted in the context of the RAILS (Roadmaps for AI integration in the raiL Sector) project. RAILS has received funding from the Shift2Rail Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.
The information and views set out in this description are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this dataset. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.
This "dataset" has been created for scientific purposes only to study the potentials of Deep Learning (DL) approaches when used to analyse Video Data in order to detect possible obstacles on rail tracks and thus avoid collisions. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.
Objectives of the Study
RAILS defined some pilot case studies to develop Proofs-of-Concept (PoCs), which are conceived as benchmarks, with the aim of providing insight towards the definition of technology roadmaps that could support future research and/or the deployment of AI applications in the rail sector. In this context, the main objectives of the specific PoC "Vision-Based Obstacle Detection on Rail Tracks" were to investigate: i) solutions for the generation of synthetic data, suitable for the training of DL models; and ii) the potential of DL applications when it comes to detecting any kind of obstacles on rail tracks while exploiting video data from a single RGB camera.
A Brief Overview of the Approach
A multi-modular approach has been proposed to achieve the objectives mentioned above. The resulting architecture includes the following modules:
The Rails Detection Module (RDM) detects rail tracks. The output of the RDM is used by the ODM and ADM.
The Object Detection Module (ODM) detects obstacles whose type is known in advance.
The Anomaly Detection Module (ADM) identifies any possible anomaly on rail tracks. These include obstacles whose type is not known in advance.
The Obstacle Detection Module merges the outputs from the ODM and the ADM.
The Distance Estimation Module estimates the distance of objects and anomalies from the train.
The research was specifically oriented at implementing the RDM-ADM pipeline. Indeed, the object detection approaches that would be used to implement the ODM have been widely investigated by the research community, instead, to the best of our knowledge, limited work has been done in the rails field in the context of anomaly detection. The RDM has been realised by adopting a Semantic Segmentation approach based on U-Net; while, to develop the ADM, a Vector-Quantized Variational Autoencoder trained in Unsupervised mode was leveraged. Further details can be found in the RAILS "Deliverable D2.3: WP2 Report on experimentation, analysis, and discussion of results".
Steps to implement the RDM-ADM pipeline and description of shared Data
The following list reports all the steps that have been performed to implement the RDM-ADM pipeline; the words in bold-italic refer to the files that are shared within this dataset:
A Railway Scenario was generated in MathWorks' RoadRunner.
A video (FreeTrackVideo) was recorded by simulating an RGB camera mounted in front of the train; no obstacles on rail tracks were considered in this phase.
2000 frames (FreeTrack2KFrames) were extracted from the aforementioned video. The video contains 4143 frames, however, only 2000 (each other frame starting from the first one) were taken into account due to training time and GPU RAM constraints.
Only 10% of the 2000 frames were manually labelled (i.e., 200 frames, a frame every 10 frames) by exploiting LabelMe; these frames were then subdivided into training and validation sets (InitialLabelledSet).
Hence, a Semi-Automatic labelling algorithm was developed by leveraging self-training and transfer learning. This algorithm made it possible to label all the FreeTrack2KFrames starting from the InitialLabelledSet. The resulting labels can be found in FreeTrack2KLabels.
Data Augmentation was then performed in order to introduce some aleatory in the dataset. Because of the same time and RAM constraints mentioned above, the FreeTrack2KFrames set of data was reduced further: 1600 frames were selected among the aforementioned 2000 and then 5 transformations (Bright, Dark, Rain, Shadow, and Sun Flare) were applied to obtain the dataset (FreeTrack16TrainSet, FreeTrack16ValSet, FreeTrack16TestSet) that was used to train, validate, and test the RDM.
Once the RDM was trained, the FreeTrackVideo was processed to obtain the masked frames that were then used to build the dataset(s) to train, validate, and test the ADM. The ADM was studied by considering two different datasets: the Non-Anomaly Dataset (NAD), which basically contains all the frames of the FreeTrackVideo once processed by the RDM; and the Augmented Non-Anomaly Dataset (A-NAD), which contains 9000 frames, 1500 of which were extracted from the NAD, while the remaining 7500 were obtained by applying the same transformations mentioned above.
Lastly, when both the RDM and the ADM were trained, the performances of the whole RDM-ADM pipeline were tested on the WithCarVideo which depicts the same scenario as the FreeTrackVideo but it also depicts a car laying on the rail tracks (i.e., an obstacle).
There is no description for this dataset.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
Proportion of children and young people in the official age range for the given level of education who are not enrolled in pre-primary, primary, secondary or higher levels of education
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The geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world. Currently 199 total entities are tracked, including all 195 UN member states, Greenland, Taiwan, Niue, and Kosovo. Comprehensive Global Administrative Zones (CGAZ) is a set of global composites for administrative boundaries. Disputed areas are removed and replaced with polygons following US Department of State definitions. It has three boundary levels ADM0, ADM1, and ADM2, clipped to international boundaries (US Department of State), with gaps filled between borders. This dataset is part of CGAZ. It was ingested from version 6.0.0 of Global Composite Files with DBF_DATE_LAST_UPDATE=2023-09-13. It shows boundaries at level ADM0 (country-level boundaries).