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51 results for “Cork”
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Carbon and water fluxes in a cork oak woodland in Central Portugal
<p>The Data set contains eddy covariance measurements of carbon and water fluxes and ancillary measurements observed at a cork oak woodland (<em>Quercus suber </em>L.) in central Portugal. The climate is Mediterranean, with mild, wet winters and hot, dry summers.</p> <p>Data are available in the file data.csv (UTF-8 encoding), the description of the variables and units are available in the file meta.csv (UTF-8 encoding).</p> <p>Further description of the site, methods and data processing can be viewed in the files metadata.pdf and metadata.csv</p> <p> </p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Organisation for Economic Co-operation and Development (OECD) data for Antalya (Turkey), Antwerp (Belgium), Cork (Ireland), Thessaloniki (Greece) (source: OECD)
<p>The data have been collected via the official OECD Application Programming Interface (API)<strong> </strong>and<strong> </strong>includes the following indicators:</p> <ul> <li>EmpPlaRes - Employment at place of residence</li> <li>LfPartRa - Labour Force and Participation rate</li> <li>UnemReg - Unemployment in regions </li> <li>RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2)</li> <li>GDPLT3 - Gross Domestic Product (Small regions TL3)</li> <li>RegEmIndu - Regional Employment by industry (ISIC rev 4)</li> <li>RegGVAWorker - Regional GVA per worker</li> <li>RegIncPC - Regional income per capita</li> </ul> <p>Source: https://data.oecd.org/api/</p>
Archaeobotanical results from Ballynacarriga 3, Cork, Ireland
<p>Dataset that resulted from the archaeobotanical analysis of samples from an archaeological excavation of a multi-period site (primarily Late Neolithic) at Ballynacarriga 3, Co. Cork, Ireland. Saved as .csv file.</p>
Archaeobotanical results from Mitchelstown 1, Cork, Ireland
<p>.csv file with results of archaeobotanical analysis from Mitchelstown 1, County Cork, Ireland. This dataset has been subject to minor modifications after peer review. This file supersedes the version published at DOI:10.5281/zenodo.7702 (https://zenodo.org/record/7702#.UuUbs9JFDwc).</p>
Potential and realized distribution at 30m for Cork oak (Quercus suber) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the cork oak (<em>Quercus suber, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.suber_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.suber</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Archaeobotanical results from Mitchelstown 1, Cork, ireland
<p>.csv file with results of archaeobotanical analysis from Mitchelstown 1, Cork, Ireland</p>
Archaeobotanical results from Gortore , Cork, Ireland
<p>Dataset with results from identified charred plant remains from Gortore 1, Cork Ireland. These were from samples taken during the excavation of an Early Neolithic house, from a Bronze Age pit and from miscellaneous features which potentially date to a much later period, such as a ditch.See the plant remains report (http://dx.doi.org/10.6084/m9.figshare.928161) and the excavation report (http://eachtra.ie/new_site/wp-content/uploads/2010/07/E2119Gortore-N8RF.pdf) for more details.</p>
Archaeobotanical results from Caherdrinny 3, Co. Cork, Ireland
<p>Identified charred plant remains from a multi-period site in north Cork, Ireland saved as a .csv file.</p>
Archaeobotanical results from Ballynamona 2, Cork, Ireland
<p>.csv file containing the results of archaeobotanical analysis on samples from a Middle/Late Bronze Age site at Ballynamona, County Cork, Ireland. This dataset has been peer-reviewed and, as a result, has been subject to minor modifications. This version supersedes the version published at DOI:10.5281/zenodo.7701 (https://zenodo.org/record/7701#.UuUbjtJFDwc).</p> <p> </p>
Plant remains (identifications) from Ballinglanna North 3, Co. Cork Ireland
<p>.csv file of identification and quantification of plant remains from Early Neolithic and Bronze Age site at Ballinglanna North 3 in north County Cork, Ireland.</p>
Plant remains (identifications) from Gortore 1b, Cork, Ireland
<p>.csv file with a record of plant remains retrieved from samples taken during excavations at Gortore 1b, CO. Cork, Ireland. Samples were taken from contexts dating to the Mesolithic, Early Neolithic and early medieval period.</p>
Archaeobotanical remains from Ballynamona 2, Cork, Ireland
<p>.csv file containing the results of archaeobotanical analysis on samples from a Late Bronze Age settlement site at Ballynamona 2, Cork, Ireland. As a result of minor modifications made after peer review, this dataset has been superseded by DOI 10.5281/zenodo.8563.</p>
Sea Level observations from Passage West, Cork, Ireland in 1842
<p>Data are described in Pugh, D. T., Bridge, E., Edwards, R., Hogarth, P., Westbrook, G., Woodworth, P. L., & McCarthy, G. D. (2021). Mean Sea Level and Tidal Change in Ireland since 1842: A case study of Cork. Ocean Science Discussions, 1-26. </p> <p>Location: Passage West, Co. Cork, Ireland <br> Latitude: 51.871 <br> Longitude: -8.334 <br> Height is given in ft ODD and m ODM <br> ODM is Ordnance Datum Malin and is current national datum used in Ireland. ODD is Ordnance Datum Dublin and is a historical datum. See paper for details on tranfer between datums <br> Time is stored as hh:mm:ss <br> Missing Values = -9999 <br> Data were gather from tide pole readings. Missing data indicate times when observations were not taken. </p>
REALISE CCUS webinar #3: The Cork Cluster study
<p>This webinar provides an overview of findings from a recent study of the Cork industrial cluster in Ireland. Participants will hear details of the industries involved: ESB Aghada and BGE Whitegate generation stations and Irving Oil Whitegate Refinery. There will be details of the role of carbon capture and storage (CCS) and how this might be configured, from the point of CO<sub>2</sub> capture to transport and long-term geological storage. Cost-benefit analysis will also be considered and there will be a discussion of different approaches to public engagement and best practices that influence the social acceptability of CCS projects.</p> <p>This free online event will be chaired by Inna Kim of SINTEF (REALISE Project Coordinator) and features presentations by Paul Murphy of Ervia (REALISE Work Package 3 Lead) and Paola Velasco-Herrejón of University College Cork (REALISE Work Package 4). There will be a 20-minute Q&A.</p>
Research Data Stewardship Survey - University College Cork
<p>This survey aimed to help us gain an understanding of research data stewardship activities in UCC, the scope of those activities, identify any gaps in current resources and skills and work out where the Research Data Service fits with related roles and services. We hoped this activity would also help with the development of a data stewardship network across UCC for support, skills sharing, peer learning and the development of tailored skills development programs within UCC. It would also provide an evidence base to inform the model UCC should adopt in meeting its future research data requirements.</p> <p><br>Funders and publishers increasingly require researchers to formally manage their data and encourage or mandate FAIR and/or Open Data outputs. Both the National and European Codes of Research Conduct recognise that data management is central to research integrity and the quality and trustworthiness of research outputs across all disciplines. Research infrastructures in Europe are currently in a phase of development with continued expansion of the European Open Science Cloud (EOSC) and related<br>services. Successive reports internationally (Realising the EOSC, 2016, Turning FAIR into a Reality, 2018) and our own recently compiled National Landscape Report (NORF, 2021) highlighted a resource and skills gap in meeting the expectations and potential of FAIR research data and related research<br>infrastructures. Specifically, in relation to FAIR and Open Data, a set of skills, competencies, and responsibilities have been identified and grouped together under the umbrella of a new “Research Data Steward” role. Research data stewardship encompasses all the various tasks and responsibilities that<br>relate to research data management throughout the entire research lifecycle. The role of data steward is not universally defined yet and is influenced by the context and the needs of the researcher or unit. Across Europe, Research Performing Organisations have taken concrete steps to address this gap, for example by appointing new data steward positions or by re-focusing existing institutional skills and supports into designated competency centres for research data supports. TU Delft is an exemplar where eight newly established embedded data stewards, with domain expertise in the relevant faculty, complement a similar number of support staff based in central services such as the Library and IT Services.</p> <p><br>In UCC the Research Data Service provides a range of data stewardship supports to the research community from advisory to tailored training. The Research Data Service and Research Data Coordinator work closely with related services and roles to provide holistic advice on research data management to the UCC research community. The Clinical Research Facility–Cork has also developed a data stewardship service which is available on a consultancy basis to funded human focused research projects. However, the ask of researchers in terms of funder mandated data management plans and commitments to FAIR and Open Data continues to increase. Certainly in the case of the Research Data Service full capacity is fast approaching. As funders embed Open Science, and by extension data management, FAIR, and Open Data more firmly in their policies and requirements there is a risk that this will impact the competitiveness of our funding applications and the reach, impact and quality of our research outputs if we cannot meet researchers increasingly complex needs for research data stewardship support.</p> <p>We know that there are those engaged in research data stewardship activities throughout UCC although this may not be reflected in their job title. Those who engage in research data stewardship activities do not always identify as Data Stewards but contribute significantly to the data management lifecycle associated with research projects. Each stage of a research project can have specialist data stewardship requirements - these tasks are performed by people in a range of roles and positions including researchers, project managers, data managers, statisticians and data analysts, research assistants, technicians, systems administrators, or research software engineers to name but a few. To develop a holistic and coordinated approach data stewardship and research data management we needed to hear from the whole research ecosystem, those engaged in research and those facilitating it.</p>
Рис. 1. А — рыжеухий бюΛьбюΛь на боярышнике, с. Δазо, Приморский край, 5.11.2019. Фото В. П. Шохрина; B — рыжеухий бюΛьбюΛь пьет сок кΛена приречного, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 03.04.2022. Фото À. А. БеΛяева; C — рыжеухий бюΛьбюΛь кормится ягоΑами Αевичьего винограΑа пятиΛисточкового, г. ВΛаΑивосток, 02.01.2020. Фото А. П. ХоΑакова; D — рыжеухий бюΛьбюΛь, кΛ. ФореΛевый, окрестности с. ФиΛипповка, Хасанский район, Приморский край, 01.03.2018. Фото Ю. А. Àармана; E — рыжеухий бюΛьбюΛь кормится ягоΑами бархата амурского, г. ВΛа- Αивосток, 06.11.2019. Фото А. В. ВяΛкова; F — рыжеухий бюΛьбюΛь, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 04.05.2022. Фото М. В. МасΛова Fig. 1. A — brown-eared bulbul on a hawthorn tree, Lazo village, Primorsky Region, 5.11.2019. Photo by V. P. Shokhrin; B — brown-eared bulbul drinks the juice of an Amur maple, Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 03.04.2022. Photo by D. A. Belyaev; C — brown-eared bulbul feeds on the berries of the Virginia creeper, Vladivostok, 02.01.2020. Photo by A. P. Khodakov; D — brown-eared bulbul. Forelevy spring, vicinity of Filippovka Village, Khasansky District, Primorsky Region, 01.03.2018. Photo by Yu. A. Darman; E — brown-eared bulbul feeds on the berries of an Amur cork tree, Vladivostok, 06.11.2019. Photo by A. V. Vyalkov; F — brown-eared bulbul. Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 04.05.2022. Photo by M. V. Maslov in An increase in the number of records of the brown-eared bulbul Microscelis amaurotis in the Russian Far East in recent years
Рис. 1. А — рыжеухий бюΛьбюΛь на боярышнике, с. Δазо, Приморский край, 5.11.2019. Фото В. П. Шохрина; B — рыжеухий бюΛьбюΛь пьет сок кΛена приречного, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 03.04.2022. Фото À. А. БеΛяева; C — рыжеухий бюΛьбюΛь кормится ягоΑами Αевичьего винограΑа пятиΛисточкового, г. ВΛаΑивосток, 02.01.2020. Фото А. П. ХоΑакова; D — рыжеухий бюΛьбюΛь, кΛ. ФореΛевый, окрестности с. ФиΛипповка, Хасанский район, Приморский край, 01.03.2018. Фото Ю. А. Àармана; E — рыжеухий бюΛьбюΛь кормится ягоΑами бархата амурского, г. ВΛа- Αивосток, 06.11.2019. Фото А. В. ВяΛкова; F — рыжеухий бюΛьбюΛь, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 04.05.2022. Фото М. В. МасΛова Fig. 1. A — brown-eared bulbul on a hawthorn tree, Lazo village, Primorsky Region, 5.11.2019. Photo by V. P. Shokhrin; B — brown-eared bulbul drinks the juice of an Amur maple, Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 03.04.2022. Photo by D. A. Belyaev; C — brown-eared bulbul feeds on the berries of the Virginia creeper, Vladivostok, 02.01.2020. Photo by A. P. Khodakov; D — brown-eared bulbul. Forelevy spring, vicinity of Filippovka Village, Khasansky District, Primorsky Region, 01.03.2018. Photo by Yu. A. Darman; E — brown-eared bulbul feeds on the berries of an Amur cork tree, Vladivostok, 06.11.2019. Photo by A. V. Vyalkov; F — brown-eared bulbul. Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 04.05.2022. Photo by M. V. Maslov
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.