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118 results for “shapefile”
Ice core, auger hole, conductivity, and shapefile data to determine bottomfast sea ice extent from lagoon sites along the Beaufort Sea Coast, Alaska, 2017-2021
The shapefile represents bottomfast sea ice (BSI) extent in lagoons along the Alaska Beaufort Sea coast during winter and spring, 2017-2021. It was created by digitizing extents from interferograms from the Alaska Satellite Facility Vertex portal. The result is used to identify BSI lateral extent in Arctic lagoons during the growth cycle seasonally. Comparing to future interferograms will identify the trend of BSI within Arctic lagoons. Each feature is attributed with applicable date range and area. Accurate data for the initial growth and maximum extent of BSI could only be collected for the winter and spring months. After the last collection in the spring, there is likely still BSI; however, the surface processes that take place after this point prevent further readings. For early winter time periods, if there are interferograms available (2017 and 2018 data had gaps in interferogram collection as Sentinel-1 was still new), the first date collected can be considered the onset of BSI formation. Ice cores are collected using a Snow, Ice, and Permafrost Research Establishment (SIPRE) corer and measured for salinity. The data is logged in Excel format following Seasonal Ice Zone Observing Network (SIZONet) practices, making it compatible with the PySIC Python toolkit for analysis. The auger data identifies key measurements collected from in-situ observations. Data are collected along five surveys and saved as a single CSV file. The data represent a 1-D representation of each auger hole. The data are used to verify satellite interpretations of BSI extent. The apparent conductivity data includes values at three frequencies (1000 Hz, 4000 Hz, 16000 Hz) recorded during the spring of 2021 in Western Elson Lagoon. Data are saved as an EMI file, which is a CSV format with specific column names and header information. MATLAB scripts to read and interpret data are included in this data package. The apparent conductivity values are used to identify the boundary between floating
WSC - Yield and water table depth shapefiles from Wibu field site
Yield data from the Wibu field site combined with a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages). It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Shapefile of administrative boundaries in Glasgow, UK, around 1920
<p>This dataset consists of a shapefile of administrative boundaries (municipal wards) in Glasgow around 1920, based on 'Map of the City of Glasgow shewing Parliamentary Divisions as fixed in 1918, and Municipal Wards as fixedin 1920'. The map is held in the Glasgow City Archives, reference DTC/13/98.</p> <p>Shapefile construction was undertaken as described in the related article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>The attributes which are included in the shapefile are Ward_Num (municipal ward number) and Ward_Name (municipal ward name). Full details of the wards numbers and ward names are given in the Report of the Medical Officer of Health for the City of Glasgow for 1921, which can be accessed at<em> https://wellcomecollection.org/works/jxgvafxr/items. </em></p>
Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910
<p>This collection consists of ESRI shapefiles for Glasgow around 1910:</p> <ul> <li>sanitary district boundaries in 1903 (Sanitary_Districts.shp, etc.)</li> <li>municipal ward boundaries in 1912 (Wards_1912.shp, etc.)</li> <li>registration district boundaries within the area of the City of Glasgow in 1913 (Registration_Districts.shp, etc.)</li> <li>routes of main rivers (River Clyde and River Kelvin) around 1915 (Rivers.shp, etc.)</li> <li>route of the Glasgow Subway around 1915 (Subway.shp, etc.)</li> </ul> <p>For details of shapefile construction, please see the descriptions in the following article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>Details of construction and references to original map sources are provided in the second paragraph of the section "Geographic conversion" in the online supplementary materials of the above reference. Further information about the boundaries is provided in the caption of Figure S1 of the supplementary materials. Additional contextual information is provided in both the main text and supplementary materials.</p>
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Hubbard Brook Experimental Forest Buildings: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The building on and nearby the Hubbard Brook Experimental Forest were manually digitized. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
Hubbard Brook Experimental Forest Weirs: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. Also shown were the rain gages, stream weirs and watershed boundaries. The stream weirs were manually digitized. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hubbard Brook Experimental Forest Roads: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The roads and trails locations were manually digitized. In 2014, roads data layer was updated to reflect road locations as indicated in lidar data. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hubbard Brook Experimental Forest Peaks: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The two peaks (Mt. Cushman - elev. 3205ft; Mt. Kineo - elev. 3330-ft) were manually digitized. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hubbard Brook Experimental Forest Boundary: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The experimental forest boundary was manually digitized. The eastern boundary was truncated by a new boundary delineated on a paper diazo copy of the Hubbard Brook Watershed Map supplied by Wayne Martin of the USFS. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hubbard Brook Experimental Forest 10ft contours: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The contour lines were manually digitized from the map. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
CTE Plots KML shapefile and coordinates
The Canopy Trimming Experiment Plots KML shapefile and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Elevation Plots KML Shapefile and Coordinates
The Elevation KML shapefile and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Everham's Plots KML shapefile and coordinates
Everham's Control Plots (2), Recovery and Germination Plots and KML shapefile and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Plots_Klawinski Plots KML shapefile and coordinates
The coordinates and shapefile (kml) of the Paul Klawinsky plots near El Verde Field Station Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Howard T. Odum Radiation Plot shapefile and coordinates
The coordinates and shapefile (kml) of the Howard T. Odum's Atomic Energy Commission (AEC) The Rain Forest Project, 1963-1967 near El Verde Field Station Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
El Verde Field Station (EVFS) Area Boundary KML shapefile and coordinates
The EVFS Area Boundary shapefile (.kml) and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Europa Chaos Block Shapefiles and Geojson Files in Lower RegMap Images
<p>The included dataset includes the raw polygon shapefiles of the outlines for chaos blocks on Europa in the lower half of the RegMap images. The outlines were generated using the standard definitions of blocks and further subdivision in morphology of plates and knobs based on those previously published by Leonard et al. (2022) in which the author of this dataset is the same that created the majority of the Leonard et al. (2022) dataset. Images used to generate the outlines were the RegMap images within the Photogrammetrically Controlled Galileo Image Mosaics of Europa, produced by the USGS Astrogeology (Bland et al., 2021). The shapefiles do not include the entire metadata and will be uploaded at a later date, but information about chaos block morphology, area in sq km, lon/lat location of center, and chaos terrain location are included. Also included within this dataset are the Geojson files (produced by Marina Dunn) that complement the ArcMap shapefiles, so they could be implemented into other programs more easily. Both datasets have yet to be peer reviewed. </p><p> </p><p> </p><p><strong>References</strong></p><p>Leonard, E.J., Howel, S.M., Mills, A., Senske, D.A., Patthoff, D.A., Hay, H.C.F.C., and Pappalardo, R.T. (2022). Finding Order in Chaos: Quantitive Predictors of Chaos Terrain Morphology on Europa, Volume 49, Issue 8, doi: <a href="https://doi.org/10.1029/2021GL097309">10.1029/2021GL097309.</a></p><p>Bland, Michael T., Weller, Lynn A., Archinal, Brent A., Smith, Ethan, Wheeler, Benjamin H. (2021). Improving the Usability of Galileo and Voyager Images of Jupiter's Moon Europa, Earth and Space Science, Volume 8, Issue 12, doi: <a href="https://doi.org/10.1029/2021EA001935">10.1029/2021EA001935</a>.</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
UK Administrative Shapefiles clipped to buildings (simplified at 100m)
<p>This dataset includes a series of modified UK administrative boundary shapefiles based on the 2011 census which are intended for use in more accurate visualisation of UK geospatial data analysis. There are two key features of these shapefiles: (1) administrative shapes have been clipped to the Ordnance Survey buildings shapefile, so that in choropleth visualisations relating to demographic data filled spaces represent populated areas of the UK rather than large undifferentiated blocks. (2) Shapefiles have been simplified to reduce loading and processing time, in the case of this repository at 100m. After testing, we have settled on a procedure to render buildings layer visually comprehensible at high zoom levels, by adding a small buffer, dissolving (so that individual overlapping shapes combine into a single more easily visualised shape) and then simplifying at 150m. It is important to emphasise that because of the use of simplification (using a Ramer–Douglas–Peucker algorithm), these shapefiles are not suitable for analysis as boundaries may not be suitably precise or accurate. For users interested in the process used to generate these files you can consult the codebase deposited on <a href="https://github.com/kidwellj/uk_census_shapes_clipped">github</a>.</p> <p>Many thanks to colleagues including Alasdair Rae for recommendations on technique used here. Computations were performed using the University of Birmingham's BEAR Cloud service, which provides flexible resource for intensive computational work to the University's research community. See <a href="http://www.birmingham.ac.uk/bear">http://www.birmingham.ac.uk/bear</a> for more details. Given the massive size of datasets involved (including the district buildings vector shapefile which is 1.4gb and consists of hundreds of thousands of individual shapes), this work would have been impossible without this invaluable resource. I hope that these files will be of use to colleagues who may not have access to similar large computational arrays and make the process of visualising UK boundary and census data more accurate and efficient.</p> <p>Original files are under OGLv3 licenses. Derived data files, where possible are licensed for use under CC BY 4.0.</p> <p>Files include the following:</p> <p><em>Original unmodified data:</em></p> <ul> <li>infuse_ctry_2011.zip - original country level shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>infuse_dist_lyr_2011.zip - original local authority shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>TermsAndConditions.html - UK Data Service license details (OGLv3), applies to all the above</li> <li> GB_Postcodes.zip - UK postcode district shapes, prepared by Addy Pope, https://datashare.ed.ac.uk/handle/10283/2597</li> </ul> <p>Derived data files:</p> <ul> <li>OS_Open_Zoomstack_district_buildings.zip - buildings layer extracted from <a href="https://www.ordnancesurvey.co.uk/business-government/products/open-zoomstack">Ordnance Survey Zoomstack package</a>, licensed under <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">OGLv3</a> and exported to gpkg format.</li> <li>*_simplified_100m.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then clipped to the buildings layer.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then run against the buildings layer as a difference layer. Suitable for using as an overlay as the shapes are inverse.</li> </ul> <p>Users who wish to use these shapefiles in a reproducible research context may want to download individual files directly from this repository. To do so, you could use the following R code:</p> <pre><code># load packages require(sf) # load simplefeature data class, supercedes sp() and used for st_read # given the size and complexity even of simplified files here, ragg is highly recommended # for users on macos given inefficiencies in default R graphics device require(ragg) # create paths as needed if (dir.exists("data") == FALSE) { dir.create("data") } # download data files only if they aren't already present if (file.exists("data/infuse_dist_lyr_2011.shp") == FALSE) { download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011.zip", destfile = "data/infuse_dist_lyr_2011.zip") unzip("infuse_dist_lyr_2011.zip", exdir = "data")} local_authorities <- st_read("data/infuse_dist_lyr_2011.shp")</code></pre> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.