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2012_2024_VIIRS_Fourier Processed_1k_ER

<h4>Overview:</h4> This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data (New version updated until 2024): NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the European and North African extent. This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2024.  <h4>Process:</h4> 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>) 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. This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised similarly.    Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Extent    -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 File names: 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. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from the Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) <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>

opencc-by-4.0May 2024View details →

2001_2021_MODIS_Fourier Processed_1k_ER

<h4>Overview:</h4> This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data: NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021.  <h4>Process:</h4> 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>) 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. Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) <h4>File names:</h4> 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. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) <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> This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data: NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. 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. <h4>Process:</h4> 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>) 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. This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.    Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) File names: 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. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) <h4> </h4>

2020_2022_VIIRS_Fourier Processed_1k_ER

<h4>Overview:</h4> This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data: NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. 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. <h4>Process:</h4> 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>) 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. This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.    Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) File names: 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 -2022. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) <h4>Files can be accessed here as well. </h4>

2012_2014_VIIRS_Fourier Processed_1k_ER

<h4>Overview:</h4> This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data: NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. 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, <h4>Process:</h4> 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>) 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. This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.    Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) File names: 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. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) <h4> </h4>

2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_ER

This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data: ERA5: Total Precipitation  The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. 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)   Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2024. 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. <h4>Process:</h4> 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>)   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.    This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.  Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Extent    -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 <h4>File names:</h4> 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. The next two characters identify the channel: 20 Monthly Total Precipitation The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data Parameter Fourier Variable Image values are ERA5  A0, A1, A2, A3, Min, Max, Vr Reflectance values  monthly total precipitation in mm ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100)

opencc-by-sa-4.0Jul 2024View details →

Burmese MicroBiology 1K Dataset

<div> <h1>Burmese-Microbiology-1K</h1> <h3>Min Si Thu, min@globalmagicko.com</h3> <div>Clinical Microbiology 1K QA pairs in Burmese Language</div> Purpose</div> <div> <div>Before this  Burmese Clinical Microbiology 1K dataset, the open-source resources to train the Burmese Large Language Model in Medical fields were rare.</div> <div>Thus, the high-quality dataset needs to be curated to cover medical knowledge for the development of LLM in the Burmese language.</div> <div>Motivation</div> <div>I found an old notebook in my box. The book was from 2019. It contained written notes on microbiology when I was a third-year medical student. Because of the need for Burmese language resources in medical fields, I added more facts, and more notes and curated a dataset on microbiology in the Burmese language.</div> <div>About</div> <div>The dataset for microbiology in the Burmese language contains 1262 rows of instruction and output pairs in CSV format.</div> <div>The dataset mainly focuses on clinical microbiology foundational knowledge, abstracting basic facts on culture medium, microbes - bacteria, viruses, fungi, parasites, and diseases caused by these microbes.</div> <div>Examples</div> <div> <ul> <li>ငှက်ဖျားရောဂါဆိုတာ ဘာလဲ?,ငှက်ဖျားရောဂါသည် Plasmodium ကပ်ပါးကောင်ကြောင့် ဖြစ်ပွားသော အသက်အန္တရာယ်ရှိနိုင်သည့် သွေးရောဂါတစ်မျိုးဖြစ်သည်။ ၎င်းသည် ငှက်ဖျားခြင်ကိုက်ခြင်းမှတဆင့် ကူးစက်ပျံ့နှံ့သည်။</li> </ul> </div> <div> <ul> <li>Influenza virus အကြောင်း အကျဉ်းချုပ် ဖော်ပြပါ။,Influenza virus သည် တုပ်ကွေးရောဂါ ဖြစ်စေသော RNA ဗိုင်းရပ်စ် ဖြစ်သည်။ Orthomyxoviridae မိသားစုဝင် ဖြစ်ပြီး type A၊ B၊ C နှင့် D ဟူ၍ အမျိုးအစား လေးမျိုး ရှိသည်။</li> </ul> </div> <div> <ul> <li>Clostridium tetani ဆိုတာ ဘာလဲ,Clostridium tetani သည် မေးခိုင်ရောဂါ ဖြစ်စေသော gram-positive၊ anaerobic bacteria တစ်မျိုး ဖြစ်သည်။ မြေဆီလွှာတွင် တွေ့ရလေ့ရှိသည်။</li> </ul> </div> <div> <ul> <li>Onychomycosis ဆိုတာ ဘာလဲ?,Onychomycosis သည် လက်သည်း သို့မဟုတ် ခြေသည်းများတွင် ဖြစ်ပွားသော မှိုကူးစက်မှုဖြစ်သည်။ ၎င်းသည် လက်သည်း သို့မဟုတ် ခြေသည်းများကို ထူထဲစေပြီး အရောင်ပြောင်းလဲစေသည်။</li> </ul> </div> <div>GitHub Repository</div> <div>https://github.com/MinSiThu/Burmese-Microbiology-1K/blob/main/data/Microbiology.csv</div> </div> <div> </div> <div>Applications</div> <div> <div>Burmese Microbiology 1K Dataset can be used in building various medical-related NLP applications.</div> <div> </div> <div> <ul> <li>The dataset can be used for pretraining or finetuning the dataset on Burmese Large Langauge Models.</li> <li>The dataset is ready to use in building RAG-based Applications.</li> </ul> </div> <div>Acknowledgments</div> <div>Special thanks to magickospace.org for supporting the curation process of **Burmese Microbiology 1K Dataset**.</div> <div> </div> <div>Contact</div> <div> </div> <div>LinkedIn - https://www.linkedin.com/in/min-si-thu/</div> </div>

MOOD_2019_SurfaceWater_PROBA_1k

PROBA V 2019 Permanent and Seasonal water 1km.  Abstract: These layers were extracted for the MOOD extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050).  Several kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories:    File naming scheme:   Distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif);  Percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif); Percentage of permanent water (erprobavLLC2019pcpermwat1km.tif)   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution: seasonal and permament  Pixel values: Meter and Percentage Source:  The Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019 Software used: The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

2012_2020_VIIRS_FourierProcessed_1k_ER

<h4>Overview:</h4> This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data: NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent. This series of VIIRS data has been updated to include imagery from 2012 to 2020.    <h4>Abstract:</h4> 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>) 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. This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.  File naming scheme:   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. The next two characters identify the channel: 03 - middle infra-red 07 - daytime land surface temperature 08 - nighttime land surface temperature 14 - NDVI: Normalised Difference Vegetation Index 15 - EVI: Enhanced Vegetation Index The last two characters of each file name denote the output from Fourier processing: a0 - mean mn - minimum mx - maximum a1 - amplitude of annual cycle a2 - amplitude of bi-annual cycle a3 - amplitude of tri-annual cycle p1 - phase of annual cycle p2 - phase of bi-annual cycle p3 - phase of tri-annual cycle d1 - variance in annual cycle d2 - variance in bi-annual cycle d3 - variance in tri-annual cycle da - combined variance in annual, bi-annual, and tri-annual cycles vr - variance in raw data <h4>Files can be accessed <a href="https://tinyurl.com/tfaviirs12201k" target="_blank" rel="noopener">here</a> as well (including Global files). </h4>   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution:  8-day and 16-day  for 2012 to 2020  Pixel values Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) Source:   VIIRS NASA :VNP21A2 and VNP13A2 Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

2001_2021_ MODIS_FourierProcessed_1k_ER

Overview: This is a set of images produced by Temporal Fourier Analysis of global MODIS data NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature   Abstract: 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. This is an update of the 2001-2019 series, which adds to 2021 and is 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 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)  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   File naming scheme:    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.    The next two characters identify the channel:  03 - middle infra-red  07 - daytime land surface temperature  08 - nighttime land surface temperature  14 - NDVI: Normalised Difference Vegetation Index  15 - EVI: Enhanced Vegetation Index    The last two characters of each file name denote the output from Fourier processing:  a0 - mean  mn - minimum  mx - maximum  a1 - amplitude of annual cycle  a2 - amplitude of bi-annual cycle  a3 - amplitude of tri-annual cycle  p1 - phase of annual cycle  p2 - phase of bi-annual cycle  p3 - phase of tri-annual cycle  d1 - variance in annual cycle  d2 - variance in bi-annual cycle  d3 - variance in tri-annual cycle  da - combined variance in annual, bi-annual, and tri-annual cycles  vr - variance in raw data   <h4>Files can be accessed <a href="https://tinyurl.com/tfamodis01211k" target="_blank" rel="noopener">here</a> as well (including Global files). </h4>   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution:  8-day and 16-day  from 2001 to 2021 Pixel values Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) Source:  MODIS  NASA :MOD11A2 and MOD13A2 Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

2001_2019_MODIS_Fourier Processed_1k_ER

Overview: This is a set of images produced by Temporal Fourier Analysis of global MODIS data NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature   Abstract: 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. This is the original version of the 2001-2019 series, which 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 (https://doi.org/10.1371/journal.pone.0001408)  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   File naming scheme:  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.    The next two characters identify the channel:  03 - middle infra-red  07 - daytime land surface temperature  08 - nighttime land surface temperature  14 - NDVI: Normalised Difference Vegetation Index  15 - EVI: Enhanced Vegetation Index    The last two characters of each file name denote the output from Fourier processing:  a0 - mean  mn - minimum  mx - maximum  a1 - amplitude of annual cycle  a2 - amplitude of bi-annual cycle  a3 - amplitude of tri-annual cycle  p1 - phase of annual cycle  p2 - phase of bi-annual cycle  p3 - phase of tri-annual cycle  d1 - variance in annual cycle  d2 - variance in bi-annual cycle  d3 - variance in tri-annual cycle  da - combined variance in annual, bi-annual, and tri-annual cycles  vr - variance in raw data Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution:  8-day and 16-day  from 2001 to 2019 Pixel values Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) Source:  MODIS  NASA :MOD11A2 and MOD13A2 Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

opencc-by-4.0Apr 2021View details →

Lorient-1k

Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds. These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France). Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence. The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes. The audio is provided as third-octave spectral data and mel spectrograms (as of YAMNET). The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python.   From a python interpreter : >> import numpy as np >> s=np.load('Lorient-1k_spectralData.npy') >> print(s.shape) (30, 351, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). >> a=np.load('Lorient-1k_presence.npy') >> print(a.shape) (30, 344, 3) The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame. >> a=np.load('Lorient-1k_time_of_presence.npy') The time of presence is expressed in percents, per scene, and per source. >> print(a.shape) (30, 3) The audio files are also available in the form of 16bits 44.1kHz wav files. Audio files are named in the same order as the first dimension of the .npy files : 00x.wav third-octaves and time of presence evaluation are accessed using s[x-1, :, : ] and a[x-1, :, : ]

opencc-bySep 2019View details →

Tombstone 013 1k

I have been doing 3D scanning for some time, but so far only in the style of delivering a hipoly textured model to the customer and the game-ready model was done by themselves. That's nice, but I would like to move it further and offer fully processed models, and also I plan to make my own model packs and offer it to the Unreal marketplace / Unity asset store. Before I get into this, I would like to ask for some feedback on one example model that I just finished from people who have been in the business for some time. I am not so much about feedback as it looks, but the technical processing - texture, model itself, etc. As a reward, you can use it in your projects if you like it;). Thanks a lot. Source: Objaverse 1.0 / Sketchfab

AbdomenCT-1K: Weakly Supervised Learning Benchmark

This is the dataset of AbdomenCT-1K: Weakly Supervised Learning Benchmark. Related paper: <a href="https://ieeexplore.ieee.org/document/9497733/">https://ieeexplore.ieee.org/document/9497733/</a> Benchmark homepage: https://abdomenct-1k-weaklysupervisedlearning.grand-challenge.org/

AbdomenCT-1K: Fully Supervised Learning Benchmark

This is the dataset of AbdomenCT-1K: Fully Supervised Learning Benchmark. Related paper: https://ieeexplore.ieee.org/document/9497733/ Benchmark homepage: https://abdomenct-1k-fully-supervised-learning.grand-challenge.org/

Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem

Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem Paper: https://ieeexplore.ieee.org/document/9497733/

AbdomenCT-1K: Semi-supervised Learning Benchmark (Subtask 1)

This is the Subtask 1 dataset of AbdomenCT-1K: Semi-supervised Learning Benchmark. Related paper: https://ieeexplore.ieee.org/document/9497733/ Benchmark homepage: https://abdomenct-1k-semi-supervised-learning.grand-challenge.org/Home/

Image Data Part 2 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem

Image Data Part 2 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem Paper: <a href="https://ieeexplore.ieee.org/document/9497733/">https://ieeexplore.ieee.org/document/9497733/</a>

AbdomenCT-1K: Semi-supervised Learning Benchmark (Subtask 2 Part 1)

This is the Subtask 2 (Part 1) dataset of AbdomenCT-1K: Semi-supervised Learning Benchmark. Related paper: https://ieeexplore.ieee.org/document/9497733/ Benchmark homepage: https://abdomenct-1k-semi-supervised-learning.grand-challenge.org/Home/

AbdomenCT-1K: Semi-supervised Learning Benchmark (Subtask 2 Part 2)

This is the Subtask 2 (Part 2) dataset of AbdomenCT-1K: Semi-supervised Learning Benchmark. Related paper: https://ieeexplore.ieee.org/document/9497733/ Benchmark homepage: https://abdomenct-1k-semi-supervised-learning.grand-challenge.org/Home/