Find research datasets worth reusing

opencc-by-4.0May 2016View details →

Fig. 4 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data

Fig. 4. Distribution of resources (light bars) and distribution of resources used by P. major (grey bars).

opencc-by-4.0May 2016View details →

Fig. 5 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data

Fig. 5. Distribution of pseudo absence cells: a — the distance to the presence cells is not less than 1000 meters; b — the distance to the presence cells is not less than 500 meters; c — the distance to the presence cells is not less than 250 meters; d — distance to the presence cells is not less than 100 meters.

opencc-by-4.0May 2022View details →

Raw data: quantum interference of identical photons from remote GaAs quantum dots

This is the raw data supporting the findings in the letter titled "Quantum Interference of Identical Photons from Remote GaAs Quantum Dots" that is published in Nature Nanotechnology.

opencc-by-4.0May 2022View details →

A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite crust: Data archive and remote sensing toolkit

This archive contains data and maps accompanying the journal article "Multispectral discrimination of spectrally similar hydrothermal minerals in mafic crust: A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite". The archive includes the full resolution, multi-format alteraton maps of hydrothermal alteration of the entire Oman–UAE ophiolite crust generated by ASTER remote sensing. Additional files necessary to reproduce or build on this work are also provided, constituting a remote sensing toolkit for the Oman–UAE ophiolite. A complete list of contents is provided within. Please contact TMB in case of compatibility issues.

opencc-by-4.0Jun 2022View details →

DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals

Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals"

opencc-by-4.0Jul 2022View details →

Data for remote sensing tool calibration

This dataset contains the in-situ data and the extracted pixel band information used to calibrate and develop an open-source remote sensing tool. The remote sensing tool provides near real-time water quality conditions of lakes/reservoirs in the USA.

CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing

Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. Sci Data 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x   The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m3/m3 to 0.014 m3/m3 and on average is close to a desired SM product stability threshold of 0.01 m3/m3 per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m3/m3.

HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru

The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.

Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)

This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading) Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/8pbO3N9IQKM

opencc-by-4.0Nov 2021View details →

Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)

This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust) Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/ANZKGxj87E0

Figure 5 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 5. The relationship between UV Aerosol Index extracted from Sentinel-5 imagery and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 6 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 6. The relationship between daily CHIRPS-precipitation and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 9 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 9. The relationship between NDVI (10 m) provided form Sentinal-2 and density of spider mite during monitoring windows based on ANOVA for linear regression. The alphabetical letters indicate of the sequence windows from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 4 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 4. Distribution maps of spider mite based on IDW model during monitoring windows, a–n are the sequence windows form June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite population data).

Figure 8 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 8. The relationship between MODIS-Evapotranspiration and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020. (First window, May 30 to June 9 was not spider mite distribution data).

Figure 3 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 3. Spider mite distribution throughout Golestan province; 6 (min.) × 6 (min.) grid cells in the DMS coordinate system (yellow points indicate the monitoring fields).

Figure 3 in Elasmobranch diversity across a remote coral reef atoll revealed through environmental DNA metabarcoding

Figure 3. Spatial variation in elasmobranch abundance and diversity inferred from eDNA metabarcoding of surface (A) and deep (40 m) (B) water samples collected around Diego Garcia. Negaprion acutidens is not visible in the charts as a result of low copy number, but was detected at site 8 in surface samples. Numbers correspond to the site numbers detailed in Figure 1.

Figure 4 in Elasmobranch diversity across a remote coral reef atoll revealed through environmental DNA metabarcoding

Figure 4. Venn diagram showing the overlap of shark species detected in previous UVC and BRUVS surveys in the MPA and the eDNA samples from around Diego Garcia analysed in this study.

opencc-by-4.0Oct 2022View details →

Remote internal wave forcing of regional ocean simulations near the U.S. West Coast

This dataset contains data from ROMS simulations near the U.S. West Coast with realistic atmospheric forcing, tidal forcing and remote internal wave forcing. FS800b has remote internal wave forcing while FS800a does not have remote internal wave forcing at the open boundaries. The ROMS simulations have 4-km horizontal resolution and 60 sigma-layers. All data is along the local x- and y- axis of the rotated C-grid. Data is stored in netcdf format. We also include the altimeter dataset (see Buijsman et al., 2020; doi: 10.1016/j.ocemod.2020.101656) used in validating the semidiurnal internal tides. More details and context of this data can be found in the article: https://doi.org/10.1016/j.ocemod.2022.102154 . Please cite this article along with any use of this data. Siyanbola, O.Q., Buijsman, M.C., Delpech, A., Renault, L., Barkan, R., Shriver, J.F., Arbic, B.K., & McWilliams, J.C. (2023). Remote internal wave forcing of regional ocean simulations near the U.S. West Coast, Journal of Ocean Modelling, 181, 18 p.