Reference ids for research institutions around the glob for name and acronym disambiguation. 97,795 institutions as of 3-15-2020.

This dataset series contains geographical grids of 1km grid cell size for Malta.The main attributes are a Grid ID coding the East and North coordinate of the lower left corner of each grid cell plus the hierarchy level plus the quad-tree level. And the INSPIREID of the lower grid cell corner. The grid has been created following the requirements of COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services and the INSPIRE Specification on Geographical Grid Systems - Guidelines. The grid can be used to represent all kinds of statistical and scientific values. The main application is the representation of demographic data (population). This 1km grid was provided by the Eurostat European Commission

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.

Grid

## Overview

Grid is a dataset for object detection tasks - it contains Food annotations for 2,294 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Montgomery County-Owned Facilities Map with Alpha Numeric Grid offers an enhanced visualization of county-owned facilities along with a custom grid system and facility table. Key features of the map include:Custom Alpha Numeric Grid: An alphanumeric grid surrounding the map frame, with letters along the top and numbers along the left side, providing a reference for locating facilities on the map.Facility Table: A table adjacent to the map frame, listing each county-owned facility by name, address, and grid ID. Users can quickly search for a facility by name in the table and locate its position on the map using the corresponding grid ID.Facility Labels: Each county-owned facility is labeled on the map with its corresponding grid ID, enhancing spatial awareness and navigation.Map Insets: Map insets for areas with a high concentration of facilities within a small area, ensuring clarity and detail for densely populated regions.The Montgomery County-Owned Facilities Map with Alpha Numeric Grid is optimized for printing at Arch E size (36x48 inches) and is available in Adobe PDF format. Users may need Adobe Acrobat for viewing and printing.Data Sources:Facility Locations: Montgomery County Property Management DepartmentGrid System: Customized grid system developed by Montgomery County GIS DepartmentAccess Requirements: Access to the Montgomery County-Owned Facilities Map with Alpha Numeric Grid is open to the public and stakeholders interested in county-owned properties and facilities.

About this dataset

The data is made available by OSF (https://osf.io/by5hu/). This data set contains precisely time-stamped frequency data from several power grids around the world in one-second resolution. The data originally is available for different dates and times. The data is available for just one hour 11 AM - 12PM to make good comparisons assuming the standard demand curve for all countries, days and months.

The raw data for each city is available on OSF (https://osf.io/by5hu/)

Research Ideas

Determine how huge are frequency deviations for different parts of the world which indicates their grid stability Identify characteristic of energy storage system in order to provide the primary frequency control to stabilize the grid

Columns

This dataset provides frequency deviation data (Actual frequency - Nominal Frequency (50 or 60Hz)). The data is presented in mHz. The data is made available for just one hour of the data which is 11AM -12PM for the following cities:

Terms of Use

https://github.com/CenterForOpenScience/cos.io/blob/master/TERMS_OF_USE.md

# FiN: A Smart Grid and Powerline Communication Dataset

Within the Fühler-im-Netz (FiN) project 38 BPL modems were distributed in three different areas of a German city with about 150.000 inhabitants. Over a period of 22 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to face the increasing complex challenges in smart grids.

~~ For detailed information we would like to refer to the full paper. ~~

Attributs | FiN 1
-------- | --------
SNR measurements | 3.3 Mio
Timespan | ~2.5yrs
*Metadata* |
Sleeve count per section | ☑
Cable length, typ, cross section | ☑
Number of conductors | ☑
Year of installation | ☑
Weather by openweather | ☑

## Paper abstract
The increasing complexity of low-voltage networks poses a growing challenge for the reliable and fail-safe operation of power grids. The reasons for this are, for example, a more decentralized energy generation (photovoltaic systems, wind power, ...) and the emergence of new types of consumers (e-mobility, domestic electricity storage, ...). At the same time, the low-voltage grid is largely unmonitored and local power failures are sometimes detected only when consumers report the outage. To end the blind flight within the low voltage network, the use of a broadband over power line (BPL) infrastructure is a possible solution. In addition to the purpose of establishing a communication infrastructure, BPL also offers the possibility of evaluating the cables themselves, as well as the connection quality between individual cable distributors based on their Signal-to-Noise-Ratio (SNR). Within the Fühler-im-Netz pilot project 38 BPL modems were distributed in three different areas of a German city with about 100.000 inhabitants. Over a period of 21 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to react agilely to the increasingly complex challenges.

# FiN-Dataset release 1.0

### Content
- 68 data .npz files
- 3 weather csv files
- 2 metadata csv files
- this readme

### Summary
The dataset contains ~3.7B SNR measurements divided into 68 1-to-1 connections. Each of the 1-to-1 connections can split into additional segments, e.g. if part of a cable was replaced due to a cable break.
All 68 connections are formed by 38 different nodes distributed over three different locations. Due to data protection regulations, the exact location of the nodes cannot be given. Therefore, each of the 38 nodes is uniquely identified by an ID.

### Data
The filename specifies the location, the ID of the source node and the destination ID.
Example: "loc03_from26_to27.npz"
-> Node is in lcation 3
-> Source node is 26
-> Destination node is 27

The .npz file contains a Python dict that is structured as follows:

data_dict = {"timestamps": np.array(...), --> Nx1 Timestamps
"spectrum_rx": np.array(...), --> Nx1536 SNR assesments on 1536 channels in RX directions. Range is 0.00dB...40.00dB
"tonemap_rx": np.array(...), --> Nx1536 Tonemaps in RX directions. Range is 0...7
"tonemap_tx": np.array(...)} --> Nx1536 Tonemaps in TX directions. Range is 0...7

### Weather
In addition to the measured data, we add weather data provided by https://openweathermap.org for all three locations. The weather data is stored in CSV format and contains many different weather attributes. Detailed information on the weather data can be found in the official documentation: https://openweathermap.org/history-bulk

### Metadata
--> nodes.csv
Contains in overview of all nodes, their id, corresponding location and voltage level.

--> connections.csv
Contains all available metadata for the 68 1-to-1 connections and their individual segements.

+ year_of_installation -> year in which the cable was installed
+ year_approximated -> Indicates whether the year was approximated or not (e.g. due to missing records)
+ cable_section -> identifies the segment or section described by the metadata
+ length -> length in meters
+ number_of_conductors -> identifier for the conductor structure in the cable
+ cross-section -> cross-section of the conductors
+ voltage_level -> identifier for the voltage level (MV=mid voltage; LV=low voltage)
+ t_sleeves -> number of T-sleeves installed within a section
+ type -> cable type
+ src_id -> id of the source node
+ dst_id -> id of the destination node

A 2 kilometer terrace-density grid for the Idaho batholith study area. Number of columns is 331 and number of rows is 285. The order of the data is from the lower left to the right and then up one row.

Statistics Norway has defined grids for the use of official statistics in Norway. Using grid ID, grid statistics can be linked to maps.

The following statistics are linked to 1kmX1km and 5kmX5km grid: Business statistics Population statistics (POP) Housing statistics (DWE) Building stock (Bui) Agricultural properties (AGP) Agriculture (AGH)

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same nation or territory have the same value. The countries and territories are not official representations of country boundaries; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

The Gridded Population of the World, Version 3 (GPWv3): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same country or territory have the same value. Note that the countries and territories are not official representations of countries boundaries; rather, they represent the area covered by the statistical data as provided. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

A 2 kilometer Bouguer gravity anomaly grid for the Idaho batholith study area. Number of columns is 331 and number of rows is 285. The order of the data is from the lower left to the right and then up one row.

Gridded hydrological model river flow estimates on a 1km grid over Great Britain for the period Dec 1980 - Nov 2011. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept) and annual minima of 7-day mean river flow (years spanning Dec-Nov) (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment; https://ukscape.ceh.ac.uk/, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award.

The Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 is a raster representation of nation-states in GPWv4 for use in aggregating population data. This data set was produced from the input census units which were used to create a raster surface where pixels that cover the same census data source (most often a country or territory) have the same value. Note that these data are not official representations of country boundaries; rather, they represent the area covered by the input data. In cases where multiple countries overlapped a given pixel (e.g. on national borders), the pixels were assigned the country code of the input data set which made up the majority of the land area. The data file was produced as a global raster at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions. Each level of aggregation results in the loss of one or more countries with areas smaller than the cell size of the final raster. Rasters of all resolutions were also converted to polygon shapefiles.

Dr. Chwalowski, We just generated a modified version of the coarse node centered grid with split walls. Here the red and green sections you highlighted in the email have been merged. Please let me know if it is good so we can generate the rest of the grids and upload them. Thanks, Rajiv

The LAEI 2006 has since been superseded. Click here for the full list of releases.

Point locations at 20 metre grid spacing, used for mapping modelled concentration values for LAEI-2006 datasets.

Points represent the centre of a 20m by 20m square and can be linked to modelled vales datasets using GRID-ID field.

Due to the detailed nature of this dataset and the resulting large file size, the download file has been compressed (approx 20mb).

Click here for more information about clearing London's air.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets.

The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains grids of the 5th, 50th and 95th percentile of drawdown in the model layers as well as grids of the probability of exceeding 0.2, 2 and 5 m drawdown as estimated with the GAL AEM model.

Dataset History

In dataset GAL_AEM_dmax_v01 the drawdown at each model layer is summarised by the 5th, 50th and 95th percentile and the probability of exceeding 0.2m, 2m and 5m. This information is contained in spreadsheets 'GAL_AEM_dmax_ExcProb_Alluvium.csv', 'GAL_AEM_dmax_ExcProb_Clematis.csv' and 'GAL_AEM_dmax_ExcProb_BCB.csv'.

The information in these spreadsheets is interpolated using QuantumGIS to create a regular grid to visualise the spatial drawdown trends. The interpolated grids are stored in this dataset.

Dataset Citation

Bioregional Assessment Programme (2016) Galilee drawdown grids. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/b106283c-2347-4024-8fa7-c582292bee65.

Dataset Ancestors

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition

• Derived From Galilee model HRV receptors gdb

This sampling frame is a set of grid-based finite-area frames spanning Canada, the United States, and Mexico. The grid for the United States is broken into individual grids for the continental United States, Hawaii, and Puerto Rico. Alaska is combined with Canada into a single grid. Each country/state/territory extent consists of four nested sampling grids at 50x50km, 10x10km, 5x5km, and 1x1km resolutions. The original 10x10km continental United States grid was developed by the Forest Service, U.S. Department of Agriculture for use in the interagency "Bat Grid" monitoring program in the Pacific Northwest and was expanded program in the Pacific Northwest and was expanded across Canada, the United States, and Mexico for the North American Bat Monitoring Program (NABat). Additional grids for Hawaii and Puerto Rico were created for this data release. This vector dataset is the individual grid-based sampling grid for Alaska and Canada at a 50x50km resolution.

This geospatial dataset consists of points corresponding to the center of 50 x 50 m grid cells which were surveyed for a finite set of target invasive plant species on Deer Flat National Wildlife Refuge. Each point corresponds to a single Survey123 form which was used to record the outcomes for a grid cell. Attributes include the grid cell id, surveyor's name, the date and time the survey was conducted and species-level survey results. For each of the target species data indicate whether that species was present or not present. If a weed species is observed within a grid cell, it is present, it is assigned to one of four abundance categories: Single individual, Scattered plants, Scattered dense patches, or Dense monoculture.