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704 results for “Scotland”
Dataset - Sex and Habitat effects on Verrallia aucta parasitism in Philaenus spumarius in Scotland
<p>This csv file contains data for analysis on the sex and habitat effects on <i>Verrallia aucta</i> parasitism in <i>Philaenus spumarius</i> in Scotland. <i>P. spumarius</i> were sampled from three different habitat types within eleven sites across Scotland and molecularly screened for <i>V. aucta</i> parasitism using qPCR. Csv file contains total number of <i>P. spumarius </i>samples, total number of <i>V. aucta </i>positive <i>P. spumarius </i>samples and percent positive, per sex and habitat, within each site.</p>
River Feshie, Scotland - Geomorphic Change Detection - Example Dataset
<p>A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection from five years of repeat monitoring of the Feshie from 2003 to 2007. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/GCDwithFIS.html">FIS Error Modelling</a>) and appears in:</p> <ol> <li>Wheaton JM, Brasington J, Darby SE, Sear DA, Vericat D‡., and Kasprak A*. 2013. <a href="https://www.researchgate.net/publication/242653748_Morphodynamic_signatures_of_braiding_mechanisms_as_expressed_through_change_in_sediment_storage_in_a_gravel-bed_river">Morphodynamic signatures of braiding mechanisms as expressed through change in sediment storage in a gravel-bed river</a>. Journal of Geophysical Research - Earth Surface. DOI: <a href="http://dx.doi.org/10.1002/jgrf.20060">10.1002/jgrf.20060</a>.</li> <li>Wheaton JM, Brasington J, Darby SE, Merz JE, Pasternack GB, Sear DA and Vericat D‡. 2010. <a href="https://www.researchgate.net/publication/227526758_Linking_Geomorphic_changes_to_Salmonid_habitat_at_a_scale_relevant_to_fish">Linking Geomorphic Changes to Salmonid Habitat at a Scale Relevant to Fish. River Research and Applications</a>.26: 469-486. DOI: <a href="http://dx.doi.org/10.1002/rra.1305">10.1002/rra.1305</a>.</li> </ol> <p>. Dataset is from:</p> <ul> <li>700m braided gravel bed river in the <a href="https://www.google.com/maps/place/57%C2%B000'41.4%22N+3%C2%B054'16.1%22W/@57.0099348,-3.9000104,6821m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d57.01149!4d-3.90446">Scottish Cairngorm mountains</a>.</li> <li>5 annual surveys</li> <li>Mix of RTKGPS and Total Station</li> <li>1m cell resolution</li> </ul> <p>Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened. </p>
LAPSO PM2.5 in Scotland
<p>Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO</p> <p>Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.</p>
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
3-hourly water level records (selected high flow events) for the River Garry at Invergarry (Inverness-shire), Scotland
<p>3-hourly records of stage (water level) for the River Garry at Invergarry (Inverness-shire), Gauge A2, for selected high-flow events 1936-1940. Extracts from a record spanning the period 1936-10-01 to 1944-09-30.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records and with the assistance of local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until 2011.</p>
SARS-CoV-2 RNA levels in Scotland's wastewater
<p>Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference data set produced by this national programme, from May 2020 to February 2022.</p> <p>Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 122 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs.</p> <p>These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.</p> <p>We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. Check the README for details and the project <a href="https://biordm.github.io/COVID-Wastewater-Scotland/">COVID-WW Website</a></p>
Paired Sentinel-1 and Sentinel-2 Images for 2 Locations in Scotland and India for 2019 and 2020
<p>The dataset contains two years of coverage (2019 and 2020) for two distant geographical areas in India and in Scotland.</p> <p>If using this dataset, please cite the paper where it has been introduced:</p> <pre><code>@article{rs14061342, author = {Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos}, title = {Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery}, journal = {Remote Sensing}, volume = {14}, year = {2022}, number = {6}, article-number = {1342}, url = {https://www.mdpi.com/2072-4292/14/6/1342}, ISSN = {2072-4292}, DOI = {10.3390/rs14061342} }</code></pre> <p> </p>
Water Body Checklists 2019: Inner Seas off the West Coast of Scotland Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Inner Seas off the west coast of Scotland using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Inner Seas off the West Coast of Scotland Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Inner Seas off the west coast of Scotland using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Daily river flow records for the River Lochy (Mucomir Cut) at Gairlochy, Scotland
<p>Daily river flows of the River Lochy (Mucomir Cut) at Gairlochy. Approx grid reference NN183840</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records.</p> <p>Record spans the period 1935-10-01 to 1944-09-30 with no gaps.</p> <p>Units cubic feet per second. Based on a calibration developed from curret meter measurements applied to stage measurements taken once per day.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>The catchment is in many respects natural, but Loch Lochy has the Caledonian Canal running through it, completed in 1822.</p> <p>Subsequent to McClean's colletion of these records, the flow of the River Lochy via the Mucomir Cut was harnessed by the North of Scotland Hydro-Electric Board by the construction of the Mucomir Power Station, commissioned in 1962.</p> <p>At the time of writing (2024), the Scottish Environment Protection Agency (SEPA) operate gauges in the Lochy system at Gairlochy and Camisky.</p>
River flow and catchment rainfall records for the River Garry at Invergarry (Inverness-shire), Scotland, 1913-1915
<p>Daily mean flows for the River Garry at Invergarry (Inverness-shire), Gauge A1, spanning the period 1913-01-01 to 1915-12-31.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records, assisted by local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until May 2011.</p>
Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland"
<p>Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland" Wellcome Open Research submission</p>
Data from KdUINO Pro deployed at Loch Leven (Scotland)
<p>This publication contains a time series dataset of light measurements in two file formats (NetCDF and CSV).</p> <p>Data obtained with an array of 12 KdUINOs PRO deployed every 0.5 meters depth at Loch Leven, Scotland, during the first validation campaign for <a href="http://monocle-h2020.eu/">the H2020 MONOCLE project</a>.</p> <p>Quality Control tests applyed: Flat test, range test and spike test.</p> <p><strong>Metadata</strong></p> <ol> <li>Discovery and identification <ul> <li>site_code: loch-leven</li> <li>platform_code: kduino-leven</li> <li>data_mode: D</li> <li>title: Data from KdUINO Pro deployed at Loch Leven (Scotland)</li> <li>summary: Data obtained with an array of 12 KdUINOs PRO deployed every 0.5 meters depth. KdUINOs were instaled on 2018-08-22T11:50:00Z at Loch Leven (Scotland) during a MONOCLE Test campaign.</li> <li>naming_authority: CSIC</li> <li>id: OS_kduino-leven_201808_TS</li> <li>source: moored surface buoy</li> <li>principal_investigator: Jaume Piera</li> <li>principal_investigator_email: jpiera@icm.csic.es</li> <li>principal_investigator_url: <a href="http://www.icm.csic.es/personal-detall?idpersonal=3735">http://www.icm.csic.es/personal-detall?idpersonal=3735</a></li> <li>institution: Marine Science Institut - CSIC</li> <li>project: MONOCLE</li> <li>network: monocle</li> <li>keywords_vocabulary: custom</li> <li>keywords: light; timeseries; monocle; Loch Leven; KdUINO PRO</li> <li>comment: Data fileds generated with mooda</li> </ul> </li> <li>Geo-spatial-temporal <ul> <li>area: Scotland</li> <li>geospatial_lat_min: 56.188715</li> <li>geospatial_lat_max: 56.188715</li> <li>geospatial_lat_units: degree_north</li> <li>geospatial_lon_min: -3.374341</li> <li>geospatial_lon_max: -3.374341</li> <li>geospatial_lon_units: degree_east</li> <li>geospatial_vertical_min: 0.5</li> <li>geospatial_vertical_max: 6</li> <li>geospatial_vertical_positive: down</li> <li>geospatial_vertical_units: meter</li> <li>time_coverage_start: 2018-08-22T11:50:00Z</li> <li>time_coverage_end: 2018-08-22T15:00:00Z</li> <li>time_coverage_duration: PT4H50M</li> <li>time_coverage_resolution: PT1M</li> <li>cdm_data_type: Station</li> <li>featureType: timeSeries</li> <li>data_type: OceanSITES time-series data</li> </ul> </li> <li>Geo-spatial-temporal <ul> <li>format_version: 1.3</li> <li>Conventions: OceanSITES-1.3</li> </ul> </li> <li>Publication information <ul> <li>publisher_name: Marine Science Institut - CSIC</li> <li>publisher_email: jpiera@icm.csic.es</li> <li>publisher_url: <a href="http://www.icm.csic.es/">http://www.icm.csic.es/</a></li> <li>references: <a href="http://www.oceansites.org">http://www.oceansites.org</a>; <a href="https://monocle-h2020.eu">https://monocle-h2020.eu</a>/; MOODA: <a href="http://doi.org/10.5281/zenodo.2643207">10.5281/zenodo.2643207</a>; Original dataset: <a href="http://doi.org/10.5281/zenodo.3757669">10.5281/zenodo.3757669</a></li> <li>data_assembly_center: Marine Science Institut - CSIC</li> <li>license: Creative Commons: Attribution-ShareAlike 4.0 International</li> <li>citation: Rodero, Carlos, Bardaji, Raul, Salvador, Joaquín & Piera, Jaume. (2020). Data from KdUINO Pro deployed at Loch Leven (Scotland). H2020 MONOCLE project [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3906019">http://doi.org/10.5281/zenodo.3906019</a></li> <li>acknowledgement: MONOCLE and EMSO - Laboratorios Submarinos Profundos</li> </ul> </li> <li>Provinance <ul> <li>data_created: 2020-04-28T15:41:23Z</li> <li>data_modified: 2020-06-23T13:24:00Z</li> <li>history: 20180701T10:00:00Z KdUINOs created and configured, C. Rodero, R. Bardaji, J. Salvador; 2018-08-22T11:50:00Z data collected, C. Rodero, R. Bardaji, J. Piera; 2020-04-07T08:09:00Z creation of data files draft, R. Bardaji; 2020-06-23T13:29:00Z data files reviewed, C. Rodero, J. Piera; 2020-06-23T14:00:00Z data files corrected, R. Bardaji</li> <li>processing_level: Raw instrument data</li> <li>QC_indicator: good data</li> <li>contributor_name: C. Rodero; R. Bardaji; J. Salvador; J. Piera</li> <li>contributor_role: instrument developer, data collector, reviewer; instrument developer, data collector, editor; instrument developer; data collector, reviewer, IP</li> <li>contributor_email: rodero@icm.csic.es; bardaji@utm.csic.es jsalvador@icm.csic.es; jpiera@icm.csic.es</li> <li>DOI: 10.5281/zenodo.3906019</li> </ul> </li> </ol> <p><strong>Vocabulary</strong></p> <ol> <li>TIME <ul> <li>standard_name: time</li> <li>units: number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT)</li> <li>axis: T</li> <li>long_name: time of measurement</li> <li>valid_min: -9223372036854775808</li> <li>valid_max: 9223372036854775808</li> <li>QC_indicator: good data</li> <li>processing_level: Instrument data that has been converted to geophysical values</li> <li>uncertainty: nan</li> <li>comment: Time is going be visualized as YYYY/MM/DD hh:mm:ss</li> </ul> </li> <li>TIME_QC <ul> <li>long_name: quality flag for TIME</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>DEPTH <ul> <li>standard_name: depth</li> <li>units: meters</li> <li>positive: down</li> <li>axis: Z</li> <li>reference: sea_level</li> <li>coordinate_reference_frame: urn:ogc:def:crs:EPSG::5831</li> <li>long_name: Depth of measurement</li> <li>_FillValue: nan</li> <li>valid_min: -10</li> <li>valid_max: 12000</li> <li>QC_indicator: good data</li> <li>processing_level: Data has been scaled using contextual information</li> <li>uncertainty: nan</li> <li>comment: Depth calculated from the sea surface</li> </ul> </li> <li>DEPTH_QC <ul> <li>long_name: quality flag for DEPTH</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>CCLEAR <ul> <li>standard_name: counts_clear</li> <li>units: counts</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Mean of number of pulses generated in the PAR region during a time slot</li> <li>QC_indicator: good data</li> <li>processing_level: Ranges applied, bad data flagged</li> <li>valid_min: 0</li> <li>valid_max: 65535</li> <li>comment: CCLEAR vocabulary is not standard</li> <li>ancillary_variables: CCLEAR_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: point</li> <li>DM_indicator: D</li> <li>sensor_model: KdUINO PRO</li> <li>sensor_manufacturer: Marine Science Institut - CSIC</li> <li>sensor_reference: https://ams.com/tcs34725</li> <li>sensor_mount: mounted_on_mooring_line</li> <li>sensor_orientation: upward</li> </ul> </li> <li>CCLEAR_QC <ul> <li>long_name: quality flag for CCLEAR</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>CRED <ul> <li>standard_name: counts_red</li> <li>units: counts</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Mean of number of pulses generated in the red region during a time slot</li> <li>QC_indicator: good data</li> <li>processing_level: Ranges applied, bad data flagged</li> <li>valid_min: 0</li> <li>valid_max: 65535</li> <li>comment: CCLEAR vocabulary is not standard</li> <li>ancillary_variables: CRED_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: point</li> <li>DM_indicator: D</li> <li>sensor_model: KdUINO PRO</li> <li>sensor_manufacturer: Marine Science Institut - CSIC</li> <li>sensor_reference: https://ams.com/tcs34725</li> <li>sensor_mount: mounted_on_mooring_line</li> <li>sensor_orientation: upward</li> </ul> </li> <li>CRED_QC <ul> <li>long_name: quality flag for CRED</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>CGREEN <ul> <li>standard_name: counts_green</li> <li>units: counts</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Mean of number of pulses generated in the green region during a time slot</li> <li>QC_indicator: good data</li> <li>processing_level: Ranges applied, bad data flagged</li> <li>valid_min: 0</li> <li>valid_max: 65535</li> <li>comment: CGREEN vocabulary is not standard</li> <li>ancillary_variables: CGREEN_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: point</li> <li>DM_indicator: D</li> <li>sensor_model: KdUINO PRO</li> <li>sensor_manufacturer: Marine Science Institut - CSIC</li> <li>sensor_reference: https://ams.com/tcs34725</li> <li>sensor_mount: mounted_on_mooring_line</li> <li>sensor_orientation: upward</li> </ul> </li> <li>CGREEN_QC <ul> <li>long_name: quality flag for CGREEN</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>CBLUE <ul> <li>standard_name: counts_blue</li> <li>units: counts</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Mean of number of pulses generated in the blue region during a time slot</li> <li>QC_indicator: good data</li> <li>processing_level: Ranges applied, bad data flagged</li> <li>valid_min: 0</li> <li>valid_max: 65535</li> <li>comment: CBLUE vocabulary is not standard</li> <li>ancillary_variables: CBLUE_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: point</li> <li>DM_indicator: D</li> <li>sensor_model: KdUINO PRO</li> <li>sensor_manufacturer: Marine Science Institut - CSIC</li> <li>sensor_reference: https://ams.com/tcs34725</li> <li>sensor_mount: mounted_on_mooring_line</li> <li>sensor_orientation: upward</li> </ul> </li> <li>CBLUE_QC <ul> <li>long_name: quality flag for CBLUE</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>KDPAR <ul> <li>standard_name: kd_par</li> <li>units: 1/m</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Downwelling Diffuse Attenuation Coefficient of the Photosynthetically Available Radiation</li> <li>QC_indicator: good data</li> <li>processing_level: Instrument data that has been converted to geophysical values</li> <li>valid_min: 0</li> <li>valid_max: 10</li> <li>comment: KDPAR vocabulary is not standard</li> <li>ancillary_variables: KDPAR_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: max</li> <li>DM_indicator: D</li> </ul> </li> <li>KDPAR_QC <ul> <li>long_name: quality flag for KDPAR</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>KDRED <ul> <li>standard_name: kd_red</li> <li>units: 1/m</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Downwelling Diffuse Attenuation Coefficient of the Red Radiation</li> <li>QC_indicator: good data</li> <li>processing_level: Instrument data that has been converted to geophysical values</li> <li>valid_min: 0</li> <li>valid_max: 10</li> <li>comment: KDRED vocabulary is not standard</li> <li>ancillary_variables: KDRED_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: max</li> <li>DM_indicator: D</li> </ul> </li> <li>KDRED_QC <ul> <li>long_name: quality flag for KDRED</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>KDGREEN <ul> <li>standard_name: kd_green</li> <li>units: 1/m</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Downwelling Diffuse Attenuation Coefficient of the Green Radiation</li> <li>QC_indicator: good data</li> <li>processing_level: Instrument data that has been converted to geophysical values</li> <li>valid_min: 0</li> <li>valid_max: 10</li> <li>comment: KDGREEN vocabulary is not standard</li> <li>ancillary_variables: KDGREEN_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: max</li> <li>DM_indicator: D</li> </ul> </li> <li>KDGREEN_QC <ul> <li>long_name: quality flag for KDGREEN</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> <li>KDBLUE <ul> <li>standard_name: kd_blue</li> <li>units: 1/m</li> <li>_FillValue: nan</li> <li>coordinates: TIME; DEPTH</li> <li>long_name: Downwelling Diffuse Attenuation Coefficient of the Blue Radiation</li> <li>QC_indicator: good data</li> <li>processing_level: Instrument data that has been converted to geophysical values</li> <li>valid_min: 0</li> <li>valid_max: 10</li> <li>comment: KDBLUE vocabulary is not standard</li> <li>ancillary_variables: KDBLUE_QC</li> <li>resolution: 1</li> <li>cell_methods: TIME: mean; DEPTH: max</li> <li>DM_indicator: D</li> </ul> </li> <li>KDBLUE_QC <ul> <li>long_name: quality flag for KDBLUE</li> <li>flag_values: 0, 1, 2, 3, 4, 7, 8, 9</li> <li>flag_meanings: unknown, good_data, probably_good_data, potentially_correctable_bad_data, bad_data, nominal_value, interpolated_value, missing_value</li> </ul> </li> </ol>
Vulnerability tools - Highlands and Islands (UK-Scotland)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Highlands and Islands Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
FIGURE 14. 1–8 in Synoptic revision of the Silurian fauna from the Pentland Hills, Scotland described by Lamont (1978)
FIGURE 14. 1–8: Plumulites ruskini Lamont, 1978, from locality R82, Wether Law Linn Formation; 1, 2: specimen NMS G. 1979.77.17 (part and counterpart), figured by Lamont on plate XXVIII, figure 13; 3: specimen NMS G.1979.77.18, figured by Lamont on plate XXVIII, figure 14; 4: specimen NMS G.1979.77.19, figured by Lamont on plate XXVIII, figure 15; 5: handwritten label; 6: specimen NMS G.1979.77.20, figured by Lamont on plate XXVIII, figure 16; 7: specimen NMS G.1979.77.21, figured by Lamont on plate XXVIII, figure 17; 8: specimen NMS G.1979.77.22, not figured by Lamont. All specimens from the Lamont Collection, possibly re-registered from NMS G.1876.42.8 (Henderson Collection). Scale bars: 1 mm (figures 1–4, 6–8).
FIGURE 18. 1, 2 in Synoptic revision of the Silurian fauna from the Pentland Hills, Scotland described by Lamont (1978)
FIGURE 18. 1, 2: Hemiarges hendersoni (Lamont, 1948) from the Deerhope Burn, possibly the upper part of the Deerhope Formation; 1: specimen NMS G.1979.45.2, figured by Lamont on plate XXX, figures 17, 18; 2: handwritten label with information on the specimen. 3–5: Bruxaspis dealgach (Lamont, 1978) from the Deerhope Burn; 3: handwritten label with information on the specimens; 4, 5: specimen NMS G.1979.77.45.2 (cephalon, (4)) and specimen NMS G.1979.77.45.1 (pygidium (5)) from the Lamont Collection, figured by Lamont on plate XXX, figures 19 and 20. 6: Dudleyaspis lothiana (Lamont, 1948) from the Bavelaw Inlier, possibly from a small quarry near Bavelaw Castle, horizon unspecified; specimen from the British Geological Survey collection GSE14487, a latex cast is reproduced by Lamont on plate XXX, fig. 21. Image BGS © UKRI 2018 reproduced with the kind permission of the British Geological Survey, Photo P835160 from GB3D Website, http://www.3d-fossils.ac.uk/tou.html, released under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (http://creativecommons.org/licences/by-nc-sa/3.0/). 7–10: Aytounella scotica Lamont, 1978, from the Deerhope Burn, Wether Law Lin Formation; specimen NMS G.1979.77.37, external mould of the glabella (7) as figured on plate XXXI, figure 1, and view of the handwritten label (8) glued onto the sample; 9, 10: handwritten label (recto and verso) discussing the affinity of the species. Scale bars: 10 mm (figure 8); 2 mm (figures 1, 4, 6); 1 mm (figures 5, 7).
FIGURE 17. 1, 2 in Synoptic revision of the Silurian fauna from the Pentland Hills, Scotland described by Lamont (1978)
FIGURE 17. 1, 2: Cyphoproetus cf. punctillosus (Lindström, 1885) from the Deerhope Burn; 1: handwritten label with publication details; 2: specimen NMS G.1979.77.32, figured by Lamont on plate XXX, figure 11. 3, 4: Cyphoproetus (Otademus) alacer Lamont, 1978, from the Deerhope Burn, Deerhope Coral Beds, Deerhope Formation; 3: handwritten label with horizon noted as "Gutterford Burn Flagstones with Chonetes", and age noted as Lamont's "Lower Pentlandian"; 4: specimen NMS G.1979.77.33 from the Lamont Collection, figured by Lamont on plate XXX, figure 12. 5– 7: Praedechenella peeblesi (Lamont, 1948) from the Deerhope Burn, possibly Wether Law Linn Formation; 5: specimen NMS G.1979.45.3, figured by Lamont on plate XXX, figure 13; 6, 7: handwritten labels with details related to the specimen. 8–11: Youngia douglasii (Lamont, 1948) from the Deerhope Burn, possibly the lower part of the Wether Law Linn Formation; 10, 11: dorsal and side views of specimen NMS G.1979.77.38 from the Lamont Collection, figured by Lamont on plate XXX, figures 14 and 16; 8: handwritten label with information on the specimens; 9: specimen NMS G.1979.77.39 from the Lamont Collection, figured by Lamont on plate XXX, figure 15. Scale bars: 2 mm (figures 4, 5, 9); 1 mm (figures 2, 10, 11).
FIGURE 11. 1, 2 in Synoptic revision of the Silurian fauna from the Pentland Hills, Scotland described by Lamont (1978)
FIGURE 11. 1, 2: Anadontopsis cf. salteri from locality R82, Wether Law Linn Formation; 1: handwritten label (author unknown) with addendum (red ink) by A. Lamont – specimen referred to as number 8 on the label; 2: specimen NMS G.1979.45.4 from the Lamont Collection. 3: Chantrakionoceras scoticum Lamont, 1978, possibly from locality R98 (same locality as Muirheada simulans), upper part of the Wether Law Linn Formation; specimen NMS G.1876.42.68 from the Henderson Collection, figured by Lamont on plate XXVII, figure 22. Holland (2000) noted that the longitudinal ornament is placed across the page; the specimen's orientation is reproduced here as in Lamont's plate. 4, 5: Pitcairniellus rebel Lamont, 1978, from the Deerhope Burn; 4: specimen NMS G.1979.77.3 from the Lamont Collection, figured by Lamont on plate XVIII, figure 1; 5: handwritten label with information related to specimen. 6, 7: Ctenodonta cf. obesa Salter, 1861, from the Deerhope Burn; 6: handwritten label with information related to specimen; 7: specimen NMS G.1979.77.4 from the Lamont Collection, figured by Lamont on plate XXVIII, figure 2. Scale bars: 5 mm (figure 1), 2.5 mm (figures 3, 4, 7).
FIGURE 8. 1, 2 in Synoptic revision of the Silurian fauna from the Pentland Hills, Scotland described by Lamont (1978)
FIGURE 8. 1, 2: Polytropina splad Lamont, 1978, from the Deerhope Burn; 1: specimen NMS G.1982.20.3 from the Lamont Collection (figured on plate XXVII, figures 1, 2); 2: handwritten label glued on and associated with the specimen. 3–9: Oriostoma polymetis Lamont, 1978, from the Deerhope Burn, NMS G. 1876.42.59 from the Henderson Collection; 3–5: apical and side views of juvenile specimen (figured as plate XXVII, figure 5); 6–7: apical and umbilical views of mature specimen (figured on plate XXVII, figures 6, 7); 8: detail of ornaments on external moulds (figured on plate XXVII, figure 8); 9: handwritten label associated with the specimen, locality, and publication reference. Scale bars: 5 mm (figures 1, 3, 4, 6, 7); 2.5 mm (figures 5, 8).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.