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FIG. 2 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 2. — Number of new (non-marine) animal species of French Guiana's fauna described since 2000. New species collected during the Mitaraka survey are indicated in colour.
FIG. 5 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 5. — Number of holotypes collected according to the different techniques used. Abbreviations: BS, beating sheet; HC, hand collecting; PVB, cross flight intercept trap with a blue LED; PVP, idem, with pink LED; PGL, idem, with GemLight; BPT, blue pan trap; SLAM, sea and land air malaise trap; SW & NS, sweeping & net sweeping; WPT, white pan trap; YPT, yellow pan trap (for a more detailed description of the techniques, see Touroult et al. 2018).
FIG. 6 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 6. — Level of compliance with recommendations for citation of expedition, ABS authorization and deposit of specimens according to two discriminating variables (n = 90 articles).
Scenario data for article: Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future
<p>This dataset contains the background scenarios for metal supply used for the publication <a href="https://doi.org/10.1111/jiec.13181">"Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future"</a> in the Journal of Industrial Ecology (2021).</p> <p><strong>Scenario description:</strong></p> <p>These background scenarios comprise five variables for the metals of copper, nickel, zinc, and lead for the time period of 2010-2050. These variables are:<br> V1: ore grade decline and energy requirements</p> <p>V2: market shares of primary production locations</p> <p>V3: energy efficiency improvements during smelting and refining</p> <p>V4: market shares of primary production routes</p> <p>V5: market shares of primary and secondary production.<br> <br> The associated <a href="http://doi.org/10.1111/jiec.13181">article</a> in the Journal of Industrial Ecology describes the modelling assumptions and data sources of the scenarios. It also conducts impact assessments for future metal supply and low-carbon technologies with these metal scenarios as well as additional electricity supply scenarios from the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> in the background .</p> <p><strong>How to use this dataset:</strong></p> <p>The background scenarios are suitable for the life cycle inventory database of ecoinvent version 3.5 or 3.6 (allocation, cut-off by classification). They can be incorporated into ecoinvent either via the brightway-based module of <a href="https://github.com/PascalLesage/presamples">presamples</a> or using the <a href="https://github.com/LCA-ActivityBrowser">activity-browser</a> and its scenario-based calculation set-up. Thereby, they can be used as background scenarios for any other prospective LCA based on ecoinvent 3.5 or 3.6.</p> <p>Moreover, they can be combined with the electricity supply scenarios of the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> using the <a href="https://github.com/LCA-ActivityBrowser/brightway-superstructure">superstructure approach</a> of the activity-browser (<a href="https://doi.org/10.1007/s11367-021-01974-2">de Koning & Steubing 2020</a>).</p> <p>Before using the dataset, please adjust the "database" columns to the name of your database, e.g. "ecoinvent3.5", and potentially also the "key" columns.</p> <p>Versions of the scenarios applicable to ecoinvent 3.7.1 or 3.8 may be added later.</p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p>
Managing innovation - how to evaluate costs/benefits and plan for the future
<p>A guest seminar offered by SCImPULSE Foundation CFO Stef Cuijpers for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Dataset - Desiccation-rehydration measurements in bryophytes: current status and future insights
<p>This dataset is related to <strong>"Desiccation-rehydration measurements in bryophytes: current status and future insights" </strong>(Morales-Sánchez JA, Mark K, Silva-Souza JP, Niinemets Ü, 2022)</p>
Global patterns of current and future road infrastructure - Supplementary spatial data
<p><strong>Global patterns of current and future road infrastructure - Supplementary spatial data</strong></p> <p><strong>Authors:</strong> Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper</p> <p><strong>Research paper summary: </strong>Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted <em>R</em>2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.</p> <p><strong>Contents:</strong> The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a <a href="https://creativecommons.org/publicdomain/zero/1.0/deed.en">Creative Commons License (CC-0)</a> and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the <a href="https://www.globio.info/global-patterns-of-current-and-future-road-infrastructure">paper by Meijer et al (2018)</a>. Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.</p> <p>Regional coding of the other vector datasets in shapefile and ESRI fgdb format:</p> <ul> <li>Region 1: North America</li> <li>Region 2: Central and South America</li> <li>Region 3: Africa</li> <li>Region 4: Europe</li> <li>Region 5: Middle East and Central Asia</li> <li>Region 6: South and East Asia</li> <li>Region 7: Oceania</li> </ul> <p>Road density raster data:</p> <ul> <li>Total density, all types combined</li> <li>Type 1 density (highways)</li> <li>Type 2 density (primary roads)</li> <li>Type 3 density (secondary roads)</li> <li>Type 4 density (tertiary roads)</li> <li>Type 5 density (local roads)</li> </ul> <p><strong>Keyword:</strong> global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios</p>
Rebuilding green infrastructure in boreal production forest given future global wood demand
<p>Global policy for future biodiversity conservation is ultimately implemented at landscape and local scales. In parallel, green infrastructure (GI) planning needs to account for socio-economic dynamics at national and global scales. Progress towards policy goals must, in turn, be evaluated at the landscape scale. Evaluation tools are often environmental quality objectives (EQO) indicators.</p> <p>We present three management scenarios for a 100,000 hectare boreal forest landscape in Sweden in the coming 100 years. The scenarios optimize financial returns and account for downscaled projected global demand of wood given a middle-of-the road Shared Socioeconomic Pathway (SSP2). We contrast a <em>reference</em> scenario meeting the wood demand against an <em>economy</em> scenario with no upper harvest limit, and a <em>green infrastructure</em> (<em>GI</em>) scenario optimizing the levels of four EQO indicators (the area of old forest, the area of mature broadleaf-rich forest, the amount of deadwood and the density of large trees).</p> <p>EQO indicators generally reached the highest levels in the <em>GI</em> scenario and the lowest levels in the <em>economy</em> scenario. Most indicators increased further in set-asides. The financial profit was 14% lower in the <em>GI</em> and 2% higher in the <em>economy</em> than in the <em>reference</em> scenario.</p> <p>These scenarios were used in the associated publication to evaluate the future response of eleven model species from three different species groups with widely differing habitat requirements. The studied species were four bird species, six wood-decaying fungi and one lichen, all either of conservation concern or considered indicator species for forest of high conservation value. Models and data for the birds and fungi have been published previously. The model for the lichen <em>Lobaria pulmonaria</em> was created for this study; the underlying data is therefore presented here as well.</p> <p>Our study has shown that effects of global SSPs can be downscaled and accounted for in planning landscape-scale forest and conservation management. Accounting for EQO indicators in the management optimization was found to be an effective approach to reveal scenarios for reaching targets on both revenue and conservation. Rebuilding green infrastructure in the production forest is possible at a relatively minor economic cost and to the benefit of species of conservation concern.</p>
Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health
<p>Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely-related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modeled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2 °C hotter in a fossil-fueled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.</p>
Rings in Clinical Trials and Drugs: Present and Future - Datasets
<p>"Rings in Clinical Trials and Drugs: Present and Future" - Datasets from publication in Journal of Medicinal Chemistry</p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
<p><span>Ocean warming is causing shifts in the distributions of marine species, but the location of suitable habitats in the future is unknown, especially in remote regions such as the Arctic. Using satellite tracking data from a 28-year long period, covering all three endemic Arctic cetaceans (227 individuals) in the Atlantic sector of the Arctic, together with climate models under two emission scenarios, species distributions were projected to assess responses of these whales to climate change by the end of the century. While contrasting responses were observed across species and seasons, long-term predictions suggest northward shifts (243 km in summer vs. 121 km in winter) in distribution to cope with climate change. Current summer habitats will decline (mean loss: </span><span>-</span><span>25%), while some expansion into new winter areas (mean gain: +3%) is likely. However, comparing gains vs. losses raises serious concerns about the ability of these polar species to deal with the disappearance of traditional colder habitats.</span></p>
Present-day and future changes in the hydrology of the Bhagirathi Basin
<p>This repository contains the daily outputs (Jan 1, 1991 to Dec 31 2020) produced in the project SDC project. The folder 'Final_full_30yrs_baseline.rar' contains all the historical outputs generated from the SPHY model. The folder contains data in the different formats (spatial and non spatial) '.map','.csv' and '.tss'</p> <p>The folder 'Climate_change.rar' contains climate runs from (Jan 1, 2021 to Dec 31 2100) for 4 GCM-RCM and ssp combinations.</p>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Historical and future climate change fosters expansion of Australian harvester termites, Drepanotermes
<p>Past evolutionary adaptations to Australia's aridification can help us to understand potential responses of species in the face of global climate change. Here, we focus on the Australian-endemic termite genus <em>Drepanotermes</em>, which is widespread in semi-arid and arid regions of Australia. We used species delineation, phylogenetic inference, and ancestral state reconstruction to investigate the evolution of mound-building and in relation to reconstructed past climatic conditions. Our results suggest that mound-building evolved several times independently, apparently facilitating expansion into tropical and mesic regions of Australia. Strong phylogenetic signal of bioclimatic variables, especially of limiting environmental factors (e.g. precipitation of warmest quarter), indicates that climate exerts a strong selective pressure. Finally, we used environmental niche modeling to predict present and future habitat suitability for eight <em>Drepanotermes</em> species. Abiotic factors such as annual temperature contributed disproportionately to calibrations, while the inclusion of biotic factors like vegetation cover improved ecological niche models in some species. A comparison between present and future habitat suitability under two different emission scenarios revealed continued suitability of current ranges as well as substantial habitat gains for most studied species, irrespective of nesting habit, yet extensive range expansions in the near future are likely precluded by low dispersal abilities.</p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY) methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) files that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains fTMY files for 18 cities in the continental United States. The locations are representative cities for each climate zone. The data for each city is derived from six different global climate models (GCMs) from the 6<sup>th</sup> Phase of Coupled Models Intercomparison Project CMIP6- ACCESS-CM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2059 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O’Neill et al. (2020).</p> <p> </p> <p>More information about the six selected CMIP6 GCMs:</p> <p> </p> <p>ACCESS-CM2 - <a href="http://dx.doi.org/10.1071/ES19040">http://dx.doi.org/10.1071/ES19040</a></p> <p>BCC-CSM2-MR - <a href="https://doi.org/10.5194/gmd-14-2977-2021">https://doi.org/10.5194/gmd-14-2977-2021</a></p> <p>CNRM-ESM2-1- <a href="https://doi.org/10.1029/2019MS001791">https://doi.org/10.1029/2019MS001791</a></p> <p>MPI-ESM1-2-HR - <a href="https://doi.org/10.5194/gmd-12-3241-2019">https://doi.org/10.5194/gmd-12-3241-2019</a></p> <p>MRI-ESM2-0 - <a href="https://doi.org/10.2151/jmsj.2019-051">https://doi.org/10.2151/jmsj.2019-051</a></p> <p>NorESM2-MM - <a href="https://doi.org/10.5194/gmd-13-6165-2020">https://doi.org/10.5194/gmd-13-6165-2020</a></p> <p> </p> <p>Additional references:</p> <p>O’Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. <em>Nat. Clim. Chang</em>. <em>10</em>, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0</p> <p>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? <em>Earth's Future</em>, <em>10</em>, e2022EF002734. <a href="https://doi.org/10.1029/2022EF002734">https://doi.org/10.1029/2022EF002734</a></p> <p>Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, <em>Agricultural and Forest Meteorology</em>, <em>93</em>, 211-228.</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (<strong><em>West and Midwest</em></strong>)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8338549, Sept 2023. [<a href="https://zenodo.org/record/8338549">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (<strong><em>East and South</em></strong>)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8335815, Sept 2023. [<a href="https://zenodo.org/record/8335815">Data</a>]</p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, <a href="http://doi.org/10.5281/zenodo.6939750">doi.org/10.5281/zenodo.6939750</a>, Aug. 2022. [<a href="https://zenodo.org/record/6939750#.YwYzp3bMKUk">Data</a>]</p>
Opening the Future... in 60 seconds
<p>A short animated video with key details about <em>Opening the Future</em>, a collective funding model developed by COPIM's Work Package 3, in close collaboration with Central European University Press and Liverpool University Press, the first two university presses implementing the model.</p>
Introduction to the Future Blood Testing Network Plus - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ </p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/YuBsU3NDdB0</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.