The Highway Administration exports data on sharp bends in provincial highways from the Highway Basic Data Database, and in addition to CSV, provides KMZ format for reference.

Species observation data of eland (Taurotragus oyx) in Sneeuberg Nature Reserve, SA.Content:All_obs_long_latitude.csv = csv-file of all eland herd observation (longitude and latitue)Eland_projected_UTM.xls = All data/meta data collected during ground census, with projected coordinated (WGS_1984_UTM_ZONE_35s)MaxEnt input data.csv = eland observation data as was used to run MaxEnt (without duplicates)CPE_ELAND.kmz = Google Earth file of eland observations

This data package is a compilation of studies and raw datasets on root traits including root biomass, root nitrogen and phosphorus concentrations organized by different root diameters, species, soil depth, and forest types in Puerto Rico. The attached zip file contains a Word document that describes metadata including the methods, the data sources and publications used in the synthesis, and information about the content of included CSV data files. Separate KMZ and csv files are attached with the collection locations.

As part of the mFUND project ADIS, test flights and trips with various UAVs were recorded. The data was collected using the ADIS downlink. These are provided as a KMZ file (or CSV). There are three test flights and two test drives. The drones used are described in the PDF ADIS-Copter, pictures of the RC buggy are in the PDF file ADIS-Rover. The drones were flown in the Hold Height mode, so their data only includes the height changes to the set height. ADIS1 was flown at an altitude of about 60 m.ADIS2 and ADIS3 at a height of approx. 40 m. The height of ADIS4 corresponds to the height above NN. The received data contains the following information: Name: Identification of the UV Lat = Latitude Long = longitude Speed = instantaneous speed [km/h] Alt = Altitude Height [m] Hdg = Magnetic heading (tax rate) RSSI = Received Signal Strength Indication [dBm] mFUND project: ADIS, FKZ: 19F2173A

Learn the step-by-step process to start downloading the open data of the City of Mendoza. To access and download the open data of the City of Mendoza, you do not need to register or create a user account. Access to the repository is free, and all datasets can be downloaded free of charge and without restrictions. The homepage has access buttons to 14 data categories and a search engine where you can directly enter the topic you want to access. Each data category refers to a section of the platform where you will find the various datasets available, grouped by theme. As an example, if we enter the Security section, we find different datasets within. Once you enter the dataset, you will find a list of resources. Each of these resources is a file that contains the data. For example, the dataset Security Dependencies includes specific information about each of the dependencies and allows you to access the information published in different formats and download it. In this case, if you want to open the file with the Excel program, you must click on the download button of the second resource that specifies that the format is CSV. Likewise, in other sections, there are datasets with information in various formats, such as XLS and KMZ. Each of the datasets also contains a file with additional information where you can see the last update date, the update frequency, and which government area is generating this information, among other things. To access and download the open data of the City of Mendoza, you do not need to register or create a user account. Access to the repository is free, and all datasets can be downloaded free of charge and without restrictions. The homepage has access buttons to 14 data categories and a search engine where you can directly enter the topic you want to access. Each data category refers to a section of the platform where you will find the various datasets available, grouped by theme. As an example, if we enter the Security section, we find different datasets within. Once you enter the dataset, you will find a list of resources. Each of these resources is a file that contains the data. For example, the dataset Security Dependencies includes specific information about each of the dependencies and allows you to access the information published in different formats and download it. In this case, if you want to open the file with the Excel program, you must click on the download button of the second resource that specifies that the format is CSV. Likewise, in other sections, there are datasets with information in various formats, such as XLS and KMZ. Each of the datasets also contains a file with additional information where you can see the last update date, the update frequency, and which government area is generating this information, among other things. Translated from Spanish Original Text: Conocé el paso a paso para empezar a descargar los datos abiertos de la Ciudad de Mendoza. Para acceder y descargar los datos abiertos de la Ciudad de Mendoza, no necesitás realizar ningún tipo de registro ni crear un usuario. El acceso al repositorio es libre y todos los datasets se pueden descargar de manera gratuita y sin restricciones. La página de inicio cuenta con botones de acceso a 14 categorías de datos y un buscador en donde podés ingresar directamente al tema al que quieras acceder. Cada categoría de datos, refiere a una sección de la plataforma en donde vas a encontrar los distintos datasets disponibles agrupados por temática. A modo de ejemplo, si ingresamos en la sección Seguridad, dentro encontramos diferentes datasets. Una vez que ingresas al dataset, encontrarás una lista de recursos. Cada uno de estos recursos es un archivo que contiene los datos. Por ejemplo, el dataset Dependencias de Seguridad incluye información específica sobre cada una de las dependencias y te permite acceder a la información publicada en distintos formatos y descargarla. En este caso, si quieres abrir el archivo con el programa Excel deberás hacer clic sobre el botón descargar del segundo recurso que especifica que el formato es CSV. Así como también, en otras secciones hay datasets con la información en diversos formatos, como XLS y KMZ Cada uno de los datasets, contiene además una ficha con información adicional en donde podés ver la última fecha de actualización, la frecuencia de actualización y qué área de gobierno es la generadora de esta información, entre otros. Para acceder y descargar los datos abiertos de la Ciudad de Mendoza, no necesitás realizar ningún tipo de registro ni crear un usuario. El acceso al repositorio es libre y todos los datasets se pueden descargar de manera gratuita y sin restricciones. La página de inicio cuenta con botones de acceso a 14 categorías de datos y un buscador en donde podés ingresar directamente al tema al que quieras acceder. Cada categoría de datos, refiere a una sección de la plataforma en donde vas a encontrar los distintos datasets disponibles agrupados por temática. A modo de ejemplo, si ingresamos en la sección Seguridad, dentro encontramos diferentes datasets. Una vez que ingresas al dataset, encontrarás una lista de recursos. Cada uno de estos recursos es un archivo que contiene los datos. Por ejemplo, el dataset Dependencias de Seguridad incluye información específica sobre cada una de las dependencias y te permite acceder a la información publicada en distintos formatos y descargarla. En este caso, si quieres abrir el archivo con el programa Excel deberás hacer clic sobre el botón descargar del segundo recurso que especifica que el formato es CSV. Así como también, en otras secciones hay datasets con la información en diversos formatos, como XLS y KMZ Cada uno de los datasets, contiene además una ficha con información adicional en donde podés ver la última fecha de actualización, la frecuencia de actualización y qué área de gobierno es la generadora de esta información, entre otros.

Burkina Faso administrative level 0 (country), 1 (administrative region), 2 (province), and 3 (department) boundary polygon and line shapefiles and KMZ files, and gazeteer

The administrative level 0 and 1 shapefiles are suitable for database or GIS linkage to the CSV population statistics tables.

NOTE that the ITOS live services are for a previous version of the boundaries

Description: The data presented here were obtained from a survey investigating the abundance and distribution of cetaceans off Costa Rica's Pacific coast in January 2022. Surveys were carried out in accordance with the survey protocol set out by the NGO ORCA. Briefly, surveys involved a port and starboard observer at all times. The observers scanned an area from 90o on their own side to 10o on the opposite side, totalling an area of 100o each. Scanning took place visually and using binoculars. Regular rotations between members of the survey team took place to prevent fatigue. Effort, weather, GPS and sightings data were recorded throughout the duration of the survey, meaning both presence and absence data were recorded. The ship did not deviate its course in response to sightings. In total, over 32 hours of surveys were conducted over a period of 10 days, covering the majority of the Pacific Coast of Costa Rica. A total of 206 individuals from 4 different species were sighted during the survey. Surveys were conducted from the tall ship Pelican of London (owned by Seas Your Future) by experienced surveyors. Dataset: The dataste consists of 3 .csv files and 2 .kmz files. The 'Observations_Effort_Sightings' .csv file contains all effort, weather and sighting data whilst on-effort. The 'Incidental_Sightings' .csv file contains a list of incidental sightings made whilst off-effort. The 'Key' .csv files describes the abbreviations used in the other two .csv files. The .kmz files contain tracks of the ship's route during the duration of the survey. The 'Track_Points' .kmz file contains the points along the ship's track, and the 'Track_Smoothed' .kmz file contains a smoothed version of the track. Please note that the track includes time spent off-effort. Please contact the authors if you have any questions about the dataset. Acknowledgements: Thanks goes to Seas Your Future and Brasenose College, University of Oxford for funding this work. We would also like to acknowledge the crew of the Pelican of London for their support throughout the survey, particularly the Captain Ben Wheatley and First Mate Tamsin Lambert. We would further like to thank Dr Charlotte Braungardt for her assistance with planning and Hannah Gibbs, Megan Derrick, Sari Ponnet, Jeremy DeMoss, Yasmin Deter, and Emily Murphy Gray for their help with data collection.

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council within the larger Seward Peninsula, Alaska region. The data were collected in the months of July and August of 2017 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). This data package includes 26 files in a variety of formats including processed canopy and leaf spectra (.csv), processed dGPS locations (.csv and .kmz), digital photographs of spectral targets (.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA110_flmd.csv. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

A list of Place Names extracted from the ǂKhomani San | Hugh Brody Collection held by the University of Cape Town (UCT) Library.Effort has been made to geocode as many place names as possible with their geographic coordinates (Latitude & Longitude).The data set is available in three formats:• a comma separated values table (CSV); • a KMZ spatial data layer, compatible with Google Maps, Google Earth and most GIS packages; • a ZIP archive of an ESRI shapefile, compatible with most GIS packagesThis data set is incomplete. Not all resources in the collection have been processed, additional place names may be missing from the list. Geocoding was performed as accurately as our reference resources allowed, but some locations may have been misplaced.We would like to thank African Tongue and the communities of the region for their assistance with the creation of this data set.The ǂKhomani San are the first people of the southern Kalahari. They lived as hunters and gatherers in the immense desert in the northwest corner of South Africa. For them, it is a land rich in wildlife, plants, trees, great sand dunes and dry riverbeds. When the ǂKhomani San share their history, they tell a story of dispossession from their lands, erasure of their way of life, and disappearance of their language. To speak of their past is to search in memory for all that was taken from them in the colonial, apartheid and post-apartheid era. They also tell a story of reclamation and recovery of lands, language, and even of memory itself. Coordinate Reference System: Geographic Coordinate System WGS1984 (GCS WGS84)Fields - Due to software limitations diacritics were not used in field names:Place_Name: Name of placeLatitude: Latitude Ordinate GCS WGS84Longitude: Longitude Ordinate GCS WGS84Notes_Loc: Any extra information about the place name location, either from the collection or discovered by the authors.Source: The source of the geographic coordinatesLocal Name: This is the name as it may have changed locallyEng: English nameAfr: Afrikaans namekqz_Kora: Kora namenaq_Nama: Nama namengh_Nuu: Nuu nametsn_Tswana: Tswana namegla_Scottish_Gaelic: Gaelic namefra_French: French nameNotes_ling: notes of linguistic interest

Glacier Identification.csv contains the glacier ID, glacier coordinates, IDs from Moon and Joughin (2008), Region of Interests for each glaciers, and names from Bjørk et al. (2015).uncertainty_all.csv contains the uncertainties from duplicate traces and MC dropout method. Icemask.zip contains thress ice masks from in 2018, 2019, and 2020. Temporal_coverage_image_results.kmz contains the temporal coverage of images and terminus traces of each glacier. time_series_terminus_variation_new.zip contains KMZ files showing the time series of terminus variations, csv of terminus variations, figures of terminus variations. Termini.zip contains the terminus traces delineated by the deep learning method. reference_polygon_new_ID.zip contains the reference polygons for converting TermPicks traces into training label polygons. label_shape_TermPicks.zip contains all the label shapefile converted from Termpicks traces.

The dataset titled "Traffic Count Stations" falls under the domain of Transportation and is tagged with keywords such as Housing Potential, Transportation, cars, on-road-vehicles, traffic, and trucks. It is available in multiple formats including CSV, XLS, GeoJSON, and KMZ among others. The dataset was published on 29th November 2019 and spans a time period until 24th February 2025. It covers the geographical area of the Peel Region. The dataset is open for access and its location is provided. It can be accessed through the ArcGIS data service and was last accessed on 26th March 2025. The dataset is identified by the unique identifier "RegionofPeel::traffic-count-stations/about" and is available in English. A link to the dataset description and its source is provided. It has a persistent identifier but does not have a globally unique identifier. The dataset comprises 529 rows of data and is owned by the Peel Region organization. It provides point features representing the location of traffic count stations on the Regional Road system with traffic counts from 1996 to the last full year. The dataset can be accessed under the Open Data Licence for The Regional Municipality of Peel. It includes resources such as CSV, ZIP, GeoJSON, and KMZ files. The metadata for the dataset was created on 26th March 2025 and was last modified on 28th March 2025.

Zimbabwe administrative level 0 (country), 1 (province), 2 (district) and 3 (ward) boundary polygon, line, and point shapefiles, KMZ files, geodatabase, and live services, and gazetteer.

Processed by ITOS 2018 09 17. ITOS live service deployment anticipated shortly.

These shapefiles are suitable for linkage by P-code to the Zimbabwe administrative levels 0 - 2 population statistics CSV population statistics tables.

The Highway Information Database of the Highway Bureau exports data of steep slopes on provincial highways, and in addition to CSV format, the Bureau also provides KMZ format for reference.

The data on provincial highway speed limit signs (Category 5 signs) is exported from the Highway Basic Data Database of the Highway Bureau. In addition to CSV format, the bureau also provides the facility information in KMZ format for reference.

Sint Maarten administrative level 0 (constituent country) and 1 (district) boundary polygon and line shapefiles, KMZ files, and geodatabase, and gazetteer

The administrative level 0 and 1 shapefiles and geodatabase layers are suitable for database or GIS linkage to the Sint Maarten administrative level 0-2 population statistics CSV population statistics tables.

This dataset contains:

A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. Google Earth)

Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)

10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03

Data mappings from primary SCADA to csv signal names

Site substation/PMU meter data where available for the same period

Site fiscal/grid meter data where available for the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Kelmarsh wind farm here.

Most lockers are part of the Bike Locker Users' Club (BLUC), but one or two locations are not BLUCs.

Data available in .csv, MapInfo .tab, Google .kmz, and ESRI .shp file formats.

Please acknowledge the source of this information using the following attribution statement:

Contains Transport for Greater Manchester data. Contains OS data © Crown copyright and database right 2017.

The Broadcasting Services Act 1992 mandates that Commercial and Community broadcasting services are licensed to serve specific geographic areas. \r These geographic areas are referred to as Licence Areas, and are determined by the Australian Communications and Media Authority (ACMA) in Licence Area Plans (LAPs). \r The ACMA defines Licence Areas in terms of areas defined by the\r Australian Bureau of Statistics (ABS) for the\r purposes of the Australian Census. The smallest area unit currently defined\r by the ABS is the Collection District (CD).\r \r This dataset comprises the collection of current broadcast Licence Areas, and\r is made available in five forms:\r \r * the list of Australian Bureau of Statistics CD numbers making up the Licence Area definition, distributed in a comma separated value (CSV) format text file;\r * a translation of the ABS CD numbers into the names of the Local Government Areas (LGAs) and Statistical Division (SDs) within the area boundary, known as the Licence Area Description, rendered as an HTML page;\r * a geographic map displaying the Licence Area, distributed as a Portable Document Format (PDF) file;\r * a spatial dataset for use in GIS software, distributed as a ZIP archive containing ESRI Shapefile Format data files; and\r * a Google Earth Placemark (.KMZ) file viewable in the Google Earth application and other software that can display Placemark data.\r \r

A quantitative pilot study of the composition of the benthic community of macro-invertebrates on intertidal rocky platforms was undertaken to: a) provide detailed information on variation in …Show full descriptionA quantitative pilot study of the composition of the benthic community of macro-invertebrates on intertidal rocky platforms was undertaken to: a) provide detailed information on variation in biodiversity along the length of the Ningaloo Marine Park; and b) determine the appropriate design of a monitoring protocol powerful enough to determine predefined levels of change. These general overall aims were in the context of the Ningaloo Marine Park Draft Management Plan of 2004, which set out a vision of maintaining the ecological values in the Park, and protecting it from adverse human impacts. Sampling was conducted at 35 sites from July 2007 and September 2010 between the northernmost site of Mildura Wreck and southernmost site of 3 Mile Out 2. Data for the project includes: All species data for each 1m2 quadrat (1744 quadrats x 291 species): [WAMSI3.2.2bQuadratDataMay2012] Lengths of small giant clams (Tridacna maxima) mapped at 20 sites (3119 rows x 11 columns including identifying information for each length): [WAMSI3.2.2bGiantClams.csv] Physical features of the platforms in three files (32 rows x 13, 11, 10 columns): [WAMSI3.2.2bPlatformAttributes.csv] [WAMSI3.2.2bPlatformQuantative.csv] [WAMSI3.2.2bFetchAndContour.csv] GPS readings as UTM values for locations of transects used to map positions of Tridacna maxima (giant clams) (404 rows x 6 columns, including sample identification information): [WAMSI3.2.2bTridacnaUTM.csv] Location of research sites - Google Earth .kmz files: Centers of 36 sites where quadrat samples were taken 2007, 2008, 2009, 2010 and reported on in all the Research Chapters, and where physical data for Research Chapter 8 were collected. [WAMSI3.2.2b Sites.kmz] Corners of 8 sites at northern boundary of Jurabi Sanctuary Zone where data for Research Chapter 9 [WAMSI3.2.2bJurabi8Sites.kmz] Shoreward, southern corners of Transect 1 and some others for areas where Tridacna maxima were measured as reported in Research Chapter 4. [WAMSI3.2.2bGiantClams.kmz] Text and images about the 32 sites sampled in 2007 and 2009 [WAMSI3.2.2bSitePages.rtf] Data files come with metadata / descriptions of the column titles and codes.

A quantitative pilot study of the composition of the benthic community of macro-invertebrates on intertidal rocky platforms was undertaken to: a) provide detailed information on variation in biodiversity along the length of the Ningaloo Marine Park; and b) determine the appropriate design of a monitoring protocol powerful enough to determine predefined levels of change.
These general overall aims were in the context of the Ningaloo Marine Park Draft Management Plan of 2004, which set out a vision of maintaining the ecological values in the Park, and protecting it from adverse human impacts. Sampling was conducted at 35 sites from July 2007 and September 2010 between the northernmost site of Mildura Wreck and southernmost site of 3 Mile Out 2. Data for the project includes: 1. All species data for each 1m2 quadrat (1744 quadrats x 291 species): [WAMSI3.2.2bQuadratDataMay2012]

Lengths of small giant clams (Tridacna maxima) mapped at 20 sites (3119 rows x 11 columns including identifying information for each length): [WAMSI3.2.2bGiantClams.csv]

Physical features of the platforms in three files (32 rows x 13, 11, 10 columns): [WAMSI3.2.2bPlatformAttributes.csv] [WAMSI3.2.2bPlatformQuantative.csv] [WAMSI3.2.2bFetchAndContour.csv]

GPS readings as UTM values for locations of transects used to map positions of Tridacna maxima (giant clams) (404 rows x 6 columns, including sample identification information): [WAMSI3.2.2bTridacnaUTM.csv]

Location of research sites - Google Earth .kmz files: Centers of 36 sites where quadrat samples were taken 2007, 2008, 2009, 2010 and reported on in all the Research Chapters, and where physical data for Research Chapter 8 were collected. [WAMSI3.2.2b Sites.kmz]

Corners of 8 sites at northern boundary of Jurabi Sanctuary Zone where data for Research Chapter 9 [WAMSI3.2.2bJurabi8Sites.kmz] Shoreward, southern corners of Transect 1 and some others for areas where Tridacna maxima were measured as reported in Research Chapter 4. [WAMSI3.2.2bGiantClams.kmz]

Text and images about the 32 sites sampled in 2007 and 2009 [WAMSI3.2.2bSitePages.rtf]

Data files come with metadata / descriptions of the column titles and codes.