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1,764 results for “ER”
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
CO2 NEE and ER + air and soil meteorological and climate parameters in Alpine grasslands, Gran Paradiso National Park, 2017-2019
<p>The dataset “fluxes_meteoclimate_nivolet_V0” is a .csv file reporting CO<sub>2</sub> Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2700 m.a.s.l.) using the flux chamber method, during the 2017, 2018 and 2019 vegetative seasons (July-September), approximately twice a month. NEE is measured with a transparent flux chamber, while ER with a shaded chamber. Data represent the average values and the corresponding standard deviations obtained from four sites at different altitudes and geological substrate of the soil. Each average value is obtained as a mean over a set of more than 20 point-measures for each site and each sampling date. Flux data are complemented by measurements of soil temperature and volumetric water content, air temperature and moisture, and solar radiance. The four sites are characterized by soils developed over carbonates (carb) (45.500212N-7.152213E), glacial deposits (glac) (45.490167N-7.139916E), gneiss rocks (gnei) (45.490256N-7.149253E) and alluvial deposits (allu) (45.492656 N-7.146092 E).</p> <p>Other relevant shortcuts used in the .csv table: Std = Standard deviation; VWC% = Volumetric Water Content %. Meteorological and climate variables recorded during the measurement of NEE and during the measurement of ER bring the suffix NEE and ER respectively (es. Pressure_NEE (hPa) = atmospheric pressure recorded during the measurement of Net Ecosystem Exchange).</p>
2012_2024_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data <strong>(New version updated until 2024)</strong>:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2024. </p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2024. The day and night land temperature came from the 8-day VNP21A2 data, whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the time series's mean, minimum, and maximum and the error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer, and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised similarly. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 24 refers to the year timeline of 2012-2024.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from the Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Global Files can be accessed <a title="VIIRS_TFA_1224" href="https://drive.google.com/drive/folders/117aWsu-Dy83Q4yRBCxWcTqnug-pRy8za?usp=sharing" target="_blank" rel="noopener">here</a>.</h4>
2001_2021_MODIS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. </p> <h4>Process:</h4> <p>Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <h4>File names:</h4> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Global Files can be accessed <a href="https://tinyurl.com/tfamodis0121">here</a>. </h4>
2016_2018_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2016 to 2018 and was produced to compare with next three years' time series.</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2016 to 2018. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 19 refers to the year timeline of 2016-2018.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4> </h4>
2010_2023_ERA5_Precipitation_Daily_Dekadal_Monthly_Annual_5k_ER
<p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2023.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2023 . The original data is at 0.25 degree resolution and was downloaded and scaled by ERA extraction algroithms. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <p>This dataset was windowed for E4warning project to Europe and North Africa. </p>
2020_2022_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2020 to 2022 and was produced to compare with previous three years' time series.</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery form 2020 to 2022. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 19 refers to the year timeline of 2020</p> <p>-2022.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Files can be accessed here as well. </h4>
2012_2014_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2014, and was produced to compare with next three years' time series,</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2014. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 14 refers to the year timeline of 2012-2014.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4> </h4>
2001_2019_ERA5_ TotalPrecipitation_FourierProcessed_ER
<p> </p> <p><strong>Abstract:</strong></p> <p>Monthly Precipitation form the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms to 5km.</p> <p> </p> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole world<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2019. </p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2001 - 2019 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2001 to 2019. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84). The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <h4>File names:</h4> <p><br>The wd at the start of each file name indicates that the image covers Globally and ER refers to Europe, North Africa, Eurasia in the E4warning and is in geographic projection. 19 refers to the year timeline of 2001-2019.<br><br>The next two characters identify the channel:<br>20 - Monthly Total Precipitation<br><br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
2001_2022_ERA5_SoilMoisture_ER_5k
<p>Soil moisture for WNV. </p> <p><strong>Abstract: </strong></p> <p>Soil moisture data have been downloaded from the ECWMF ERA5 reanalysis dataset and then windowed to provide 5km raster datasets for the MOOD extent for the years 2001- 2022 (Oct 2022)</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>era5corsoilmoist+ Year+ Month +.tif</p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,18.0000000000000000 : 69.0000000000000000,82.0000000000000000<br><strong>Spatial resolution:</strong><br>0.25 (5000m)<br><strong>Temporal resolution:</strong><br>Monthly from 2001 to 2022</p> <p><br><strong>Pixel values</strong></p> <p>Soil Moisture Precentage</p> <p><strong>Source: </strong><br> ECWMF ERA5 Soil Moisture</p> <p><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
Arctic sea ice radar freeboard from ERS-1, ERS-2, Envisat and CryoSat-2
<p>This dataset presents a radar freeboard time series from 1993 to 2021 for Arctic sea ice. Envisat, ERS-2 and ERS-1 radar freeboards have been estimated using CryoSat-2 as a reference, they are "SAR-like" estimations as they have been calibrated on CS-2 SAR TFMRA50 radar freeboard. </p>
2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_ER
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024. This version is an update to the previous one (2010 to 2022)</p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2024.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting . The original data is at a 0.25-degree resolution and was downscaled by ERA extraction algorithms, then downloaded at a 1 km resolution. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 (Total precipitation) 1 km imagery from 2010 to 2024. Each parameter extract dataset was then processed by a Temporal Fourier Processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and errors measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Then, sea pixels were masked with a VIIRS land/sea layer in arcmap. The E4Warning study region was a subset of global images. </p> <p> </p> <p>This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p>Projection + EPSG code:</p> <p>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <h4>File names:</h4> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 04 refers to the year timeline of 2010-2024.<br><br>The next two characters identify the channel:<br>20 Monthly Total Precipitation<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons
<p>This is the data supplement for the paper entitled, "Super-resolution analysis of the origins of the elementary events of ER calcium release in dorsal root ganglion neurons"<br><br>There are two principal subdirectories within the enclosed zip file:</p><ol><li>10xEExM_data: The directory contains two exemplar datasets each of 10x Enhanced expansion microscopy images of IP3R1 and RyR immunolabelling in DRG soma, at the sub-plasmalemmal regions.<br> </li><li>Correlative Analysis: The directory contains two sub-directories of worked examples of data and correlative analysis of calcium sparks and dSTORM images of RyR and IP3R. The instructions for the code, run in IDL, are included in the Readme.txt enclosed within.</li></ol>
MOOD_2017_RoadDensity_OSM_ER
<p>Road density for unclassified, minor, major, and autoroutes 2017. </p> <p><strong>Abstract: </strong></p> <p>This dataset was produced by extracting all road data from national-level Open Steet Map archives for 2017. The length of each type of road was calculated for a series of 5 square km grids covering the MOOD study area.</p> <p> </p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -24.5705863176307275,23.6115332000000535 : 44.5127470157025726,73.0115332000000308<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br>The year 2017<br><strong>Pixel values:</strong><br> Km of road per square km <br><strong>Source: </strong><br>Open Street Map<br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
Making Archaeology FAIR[ER]
<p>In April 2018, at the Society for American Archaeology’s 83rd Annual Meeting in Washington, D.C., Sarah Whitcher Kansa, Julian Richards and Willeke Wendrich hosted a forum entitled ‘Making Archaeology Fair’ (see abstract below). The forum responded to a recently published <a href="https://www.nature.com/articles/sdata201618">article by Wilkinson et al.</a> outlining FAIR data principles, centred on avenues for making scientific data findable, accessible, interoperable and reusable. In preparation for this forum, I produced this illustrated graphic. </p>
2013_FarmLabour_ER
<p>Farm Labour. </p> <p><strong>Abstract:</strong></p> <p>Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by country. xlsx files.</p> <p><strong>Source: </strong><br>Eurostat farm Labour by NUTS <br><strong>Software used:</strong><br>Excel <br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p>
2012_2020_VIIRS_FourierProcessed_1k_ER
<h4><strong>Overview:</strong></h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data has been updated to include imagery from 2012 to 2020. </p> <p> </p> <h4><strong>Abstract:</strong></h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p><strong>File naming scheme:</strong> </p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br></p> <h4>Files can be accessed <a href="https://tinyurl.com/tfaviirs12201k" target="_blank" rel="noopener">here</a> as well (including Global files). </h4> <p><br> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> 8-day and 16-day for 2012 to 2020 </p> <p><br><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br> VIIRS NASA :VNP21A2 and VNP13A2</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
2001_2021_ MODIS_FourierProcessed_1k_ER
<p><strong>Overview: </strong></p> <p>This is a set of images produced by Temporal Fourier Analysis of global MODIS data</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p> </p> <p><strong>Abstract: </strong></p> <p>MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.</p> <p>This is an update of the 2001-2019 series, which adds to 2021 and is then processed by a temporal Fourier processing algorithm.</p> <p>A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)<br> Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format in order to give data users more flexibility<br> </p> <p><strong>File naming scheme:</strong> </p> <p> The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 11 refers to the year timeline from 2001-2021.<br> <br> The next two characters identify the channel:<br> 03 - middle infra-red<br> 07 - daytime land surface temperature<br> 08 - nighttime land surface temperature<br> 14 - NDVI: Normalised Difference Vegetation Index<br> 15 - EVI: Enhanced Vegetation Index<br> <br> The last two characters of each file name denote the output from Fourier processing:<br> a0 - mean<br> mn - minimum<br> mx - maximum<br> a1 - amplitude of annual cycle<br> a2 - amplitude of bi-annual cycle<br> a3 - amplitude of tri-annual cycle<br> p1 - phase of annual cycle<br> p2 - phase of bi-annual cycle<br> p3 - phase of tri-annual cycle<br> d1 - variance in annual cycle<br> d2 - variance in bi-annual cycle<br> d3 - variance in tri-annual cycle<br> da - combined variance in annual, bi-annual, and tri-annual cycles<br> vr - variance in raw data</p> <p> </p> <h4>Files can be accessed <a href="https://tinyurl.com/tfamodis01211k" target="_blank" rel="noopener">here</a> as well (including Global files). </h4> <p><br> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> 8-day and 16-day from 2001 to 2021</p> <p><br><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br>MODIS NASA :MOD11A2 and MOD13A2</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
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
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.