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Molecular composition of dissolved organic matter in NTL-LTER lakes detected by Fourier-transform ion cyclotron resonance mass spectrometry
The composition of dissolved organic matter (DOM) varies widely in the environment due to distinct sources of the material and subsequent processing. DOM composition drives its reactivity in terms of many processes including photochemical reactions, microbial metabolism, and carbon cycling within water bodies. This study uses ultra-high resolution mass spectrometry via a Fourier-transform ion cyclotron resonance mass spectrometer (FT-ICR MS) to evaluate DOM composition at the molecular level to determine differences in DOM composition among the NTL-LTER lakes. Whole water samples were collected from the surface of each lake near the shore on August 18th and 19th in 2016 in. Ultraviolet-visible spectra were recorded as light absorbance can also give information about DOM composition. Additionally, concentrations of anions, cations, and pH were measured waters because these can all alter DOM reactivity in the environment. Both water chemistry and DOM composition vary widely among the lakes with the bogs displaying the most terrestrial-like signature in DOM and the oligotrophic lakes show more microbial-like or environmentally processed DOM.
Molecular composition of dissolved organic matter from Lake Mendota from June – November 2017, analyzed by Fourier-transform ion cyclotron resonance mass spectrometry
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) allows for the characterization of dissolved organic matter at the molecular level. The water sample was collected near the NTL-LTER research buoy on Lake Mendota. Formula assignments were made to raw mass to charge ratios detected in the mass spectrum using a custom processing script and resulting in a list of chemical formulas making up the DOM sample.
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>
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>
Raw dataset for: High-throughput multimodal wide-field Fourier-transform Raman microscope
<p>This is the Raw spectral dataset of the data published in 10.1364/OPTICA.488860</p> <p>Data are arranged as follows:</p> <p>wavenumber [Nx1]</p> <p>Hyperspectrum_cube [Nx2, A, B]: hyperspectral datacube, where: Hyperspectrum_cube (1:N, :, :) is the real part; Hyperspectrum_cube (N+1:2N, :, :) is the imaginary part</p> <p>maximum [1x1]</p> <p>minimum [1x1].</p> <p>N: number of spectral bands</p> <p>A and B: size of the spatial coordinates</p> <p>Spectral amplitudes are obtained by: Hyperspectrum_cube=double(Hyperspectrum_cube)./(2.^16-1).*maximum+minimum</p>
Fourier-transform Infrared (FT-IR) spectroscopy fingerprints subpopulations of extracellular vesicles of different sizes and cellular origin
<p>Atomic Force Microscopy images of Large (LEV), Medium (MEV) and Small (SEV) Extrzcellular vesicles (EVs) from murine cell line B16 (B16-F10, ATCC CRL-647; Mus musculus, mouse; tissue: melanoma skin). Image size 8.3 x 8.3 um. Analysis mode: Tapping mode in air as described in Paolini et al. https://doi.org/10.1080/20013078.2020.1741174</p>
NLO QCD Track Evolution Kernels for Fourier and Wavelet Methods
<p>Two datasets of LO and NLO evolution numerical kernels for track functions, one for the Fourier series method and the other for the Legendre wavelet method. We also include the corresponding Julia code to numerically solve the track evolution equation based on these kernels. These datasets are used to build docker images for <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-fourier">Fourier</a> and <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-wavelet">wavelet</a> approaches. More details of instructions, as well as the moment method to the track evolution, can be found on <a href="https://github.com/HaoChern14/Track-Evolution">https://github.com/HaoChern14/Track-Evolution</a>. </p> <p> </p>
2001_2019_MODIS_Fourier Processed_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 the original version of the 2001-2019 series, which was 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 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 (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 to give data users more flexibility<br> </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. 19 refers to the year timeline of 2001-2019.<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><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 2019</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>
The photooxidation of dissolved organic matter in surface waters analyzed by Fourier-transform ion cyclotron resonance mass spectrometry.
Dissolved organic matter (DOM) plays an important role in carbon cycling in natural waters. The processing of DOM in these waters can occur via photooxidation, or interaction with sunlight. This processing can lead to the production of CO2, and also the alteration of organic compounds that make up DOM. It is likely that the extent of photooxidation is at least partially determined by the chemical composition of DOM. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize the dissolved organic matter at the molecular level for all water samples, both before and after light exposure to better understand the photooxidation of DOM. Chemical formulas were assigned to mass to generated mass to charge ratios using a custom script in R, resulting in a list of chemical formula assignments for each DOM sample, at multiple light exposure time points.
Data and code associated with "Fourier synthesis optical diffraction tomography for kilohertz rate volumetric imaging"
<p>Imaging data and derived analysis data used in the figures of the manuscript "F<span>ourier synthesis optical diffraction tomography for kilohertz rate volumetric imaging"</span></p>
Laser scan and polarization resolved Fourier-plane measurements of nanoparticle clusters
<p><strong>meas_00 - meas_09: measurements of particle ensembles</strong></p> <p><strong>meas_10 - meas_11: measurements of excitation beam</strong></p> <p><strong>meas_12: measurement of camera background</strong></p> <p>See "meas_readme.pdf" for more details on how to use and understand the data.</p>
Original raw data to paper "Towards Fourier Domain Mode Locked frequency combs"
<p>The file contains the 4 original datasets used in the paper. The format is HD5. The data represents transients recorded with a Keysight 63GHz real time oscilloscope. The data represents the output from fast 35GHz and 50GHz diode base photo receivers detecting beat signals of two independent lasers.</p> <p> </p> <p>The filename "#A_Bnm_Cnm_Dus.h5" contains the following information:</p> <p> - A is the number of the measurement<br> - B is the bandwidth of the FDML output in nanometers<br> - C is the wavelength of the CW laser in nanometers<br> - D is the length of the measurement in micrometers</p> <p>The data set "#9_80nm_1290nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 3)</p> <p>The data set "#10_80nm_1290nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 4)</p> <p>The data set "#17_40nm_1305nm_200us.h5" is shown in:<br> - Figure 2b, c, d, e<br> - Figure 3<br> - Figure 4a, b, c, d, e(as Measurement 1)<br> - Figure S2<br> - Figure S3<br> - Figure S4</p> <p>The data set "#18_40nm_1305nm_200us.h5" is shown in:<br> - Figure 4e (as Measurement 2)</p> <p>Each dataset contains two collections of data.<br> One is the beatsignal between FDML laser and CW laser.<br> The second is the beatsignal between the two CW lasers during the measurement time of the first beat signal.</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)
<p>With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7b. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT
<p>From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain. </p>
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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.