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3,409 results for “UK”
Interviews on Current Practices for Describing and Providing Access to UK Public Sector Web Archives
<p>This dataset contains qualitative interview data which investigated current practice for describing and providing access to UK Public Sector Web Archives. Participants included staff responsible for the management and curation of the following web archives:</p><ul><li>UK Web Archive (four of the six Legal Deposit libraries: the British Library, Bodleian Libraries, Cambridge University Library, and the National Library of Scotland)</li><li>UK Government Web Archive (The National Archives)</li><li>UK Parliament Web Archive (Parliamentary Archives)</li><li>NRS Web Archive (National Records of Scotland) and</li><li>PRONI Web Archive (Public Record Office Northern Ireland).</li></ul><p>Available to the public are the University of Dundee (UoD) ethics application for this study, including the research data management plan and information provided to organisations before participating in the study. The report of interview codes and code groups (the 'Codebook') demonstrates the connections made across responses. This is supplemented by a redacted report of quotations by code, organised by code group and document.</p><p>This qualitative interview data, and subsequent analysis, forms the basis of the Masters thesis 'Web Archives for All? Towards Equitable Access to UK Public Sector Web Archives' submitted as part of the MLitt Archives and Records Management at the University of Dundee. </p>
EOU UK central heating on/off date micro-survey result
<p>When do UK Twitter users (2017 to 2022) turn their central heating fully off, by month? This informal periodic survey on social media suggests that a substantial fraction of respondents (up to 10%) leave their central heating on year-round, which may lead to unnecessary energy consumption and carbon emissions.</p>
Moulton Packhorse Bridge, Suffolk, UK
Reprocessed old data from 2011 in RealityCapture to try and get my head around it. Created in RealityCapture by Capturing Reality from 108 images in 00h:16m:11s. Source: Objaverse 1.0 / Sketchfab
Interior of Plot 22, Sheffield, UK.
Plot 22 is a multipurpose creative space at 22 Exchange Street, Sheffield, UK. This is the right corner of the first space you go into, with art by Trik 9 (the female figure in the middle) and others. (I'd like to credit the others but I don't know their names -- please comment if you know.) There is a bench extending out from the wall underneath the Trik 9 head, but the reconstruction has not captured that. 3D reconstruction using Meshroom and Meshlab, from photos taken in March 2021. Source: Objaverse 1.0 / Sketchfab
Dinosaur footprint from Hanover Point, IOW, UK.
An ornithopod dinosaur (iguanodontian) footprint 3D model from Hanover Point, Isle of Wight, United Kingdom. Its measurements for its foot length is 59 cm and foot width is 65 cm. The pink pencil for scale is 15 cm long and is located on the middle digit. Scanned on 17th September, 2021. For academic and educational purpose only. Source: Objaverse 1.0 / Sketchfab
Hardy Tree (Camden, UK) and gravestones
This is the Hardy Tree in [St Pancras Old Church](https://goo.gl/9AiihJ), Camden, London, UK ([OSM](https://goo.gl/ydVXeA)). The tree was reportedly planted by the author Thomas Hardy and the roots are embedded in pile of gravestones.  The point cloud is a combination of a RIEGL VZ-400 scan and a ZEB-REVO (to get detail around the gravestones). Scanning done by University College London ([@kungphil](https://twitter.com/kungphil), [@mathiasdisney](https://twitter.com/mathiasdisney), [@UCLgeography](https://twitter.com/UCLgeography)). This work was funded by the NERC National Centre for Earth Observation (NCEO). Source: Objaverse 1.0 / Sketchfab
The National Archives - Richmond, UK
A videogrammetry scan of The National Archives in Richmond, UK. The scan was made in Agisoft software from 158 frames from this video: https://www.youtube.com/watch?v=ncNVLQoKTo8 Source: Objaverse 1.0 / Sketchfab
Southsea Castle Portsmouth UK
This is Southsea Castle found in Southsea, Portsmouth on the South coast of the United Kingdom. It's part of my ongoing project to use photogrammetry with my drone to make 3D models of structures for people all over the world to use how they wish and to experience as not everyone is able to travel. Enjoy! Source: Objaverse 1.0 / Sketchfab
UFLUX 100m daily photosynthesis fluxes in southwestern UK 2020
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in southeastern UK 2020
<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div>
UFLUX 100m daily photosynthesis fluxes in in southwestern UK 2021
<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2021</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div>
UFLUX 100m daily photosynthesis fluxes in northwestern UK 2021
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2021</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in southwestern UK 2022
<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2022</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div>
UFLUX 100m daily photosynthesis fluxes in southeastern UK 2021
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2021</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in northwestern UK 2022
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2022</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in northwestern UK 2020
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in southeastern UK 2022
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2022</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in northeastern UK 2020
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northeastern UK 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX 100m daily photosynthesis fluxes in northeastern UK 2021
<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northeastern UK 2021</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using<strong> machine learning models</strong>. It integrates <strong>satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. It includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>) [<em>open access</em>]</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>) [<em>in progress</em>]</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>) [<em>in progress</em>]</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>) [<em>in progress</em>]</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>) [<em>in progress</em>]</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
Urban peregrine falcon (Falco peregrinus) breeding season diet in UK, 2020–2022
<p>Diets of urban peregrine falcons in UK were monitored via nest cameras during the breeding season (March-June) from 2020–2022. All prey items were then identified to species level where possible, by Ed Drewitt. This dataset contains the prey items recorded during each year of the study and location of the sites. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.