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13 results for “impact assessment method”
Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
<p>This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (<a href="https://www.doi.org/10.1111/jiec.70023" target="_blank" rel="noopener">DOI: 10.1111/jiec.70023</a>). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository.</p> <p>For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: <a title="IMPACT World+ version 2.1" href="https://doi.org/10.5281/zenodo.14041258">https://doi.org/10.5281/zenodo.14041258</a></p> <h3>Content</h3> <p><strong>- native resolution (monthly, watershed scale):</strong></p> <ul> <li><strong>AWARE20_Native_CFs_geospatial.gpkg</strong>: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs</li> <li><strong>AWARE20_Native_CFs_geospatial.kmz</strong>: Version of <em>AWARE20_Native_CFs_geospatial.gpkg </em>for GoogleEarth</li> <li><strong>AWARE20_Native_CFs.xlsx</strong>: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting</li> <li><strong>AWARE20_Intermediate_Variables.xlsx</strong>: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.</li> <li><strong>figures_AWARE_AWARE20_comparison_all_basins.zip</strong>: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.</li> </ul> <p><strong>- spatiotemporal aggregations:</strong></p> <ul> <li><strong>AWARE20_Countries_and_Regions.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10) </a></li> <li><strong>AWARE20_Subnational_Resolution.xlsx</strong>: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (<a href="https://gadm.org/old_versions.html" target="_blank" rel="noopener">https://gadm.org/old_versions.html</a>)</li> <li><strong>AWARE20_Crop_Specific.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10)</a>, using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information.</li> </ul> <p> </p> <h3><strong>Changes:</strong></h3> <ul> <li>v1.0.1: <ul> <li>addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx)</li> </ul> </li> <li>v1.0.0 (corresponding to published article): <ul> <li>update of readme sheets with appropriate references to corresponding article</li> <li>update of reference "Müller Schmied et al. (2024)"</li> <li>added file: AWARE20_Subnational_Resolution.xlsx</li> </ul> </li> <li> v0.0.3: <ul> <li>use bug-fixed WaterGAP2.2e data from Sept 2023</li> <li>added country and subnational aggregations</li> <li>changed "NoData" to "NotDefined" in the tables</li> <li>added gridcell pHWC to intermediate variables</li> <li>corrected table of water consumption data without post-processing in "Intermediate_Variables"</li> </ul> </li> </ul> <p> </p> <h3><strong>Caveats:</strong></h3> <ul> <li>Spatial CF aggregations for treaties: <ul> <li>Due to the creation date of the data set, the <strong>BRICS aggregations </strong>in<strong> </strong><em>AWARE20_Countries_and_Regions.xlsx</em> do not include the states that joined after 2023. In <em>AWARE20_Crop_Specific.xlsx</em>, the 10-member BRICS is labeled BRICS+.</li> </ul> </li> </ul>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
IMPACT World+ / a globally regionalized method for life cycle impact assessment
<p><strong>IMPACT World+</strong> is a life cycle impact assessment method which characterizes thousands of substances spanning across various compartments and sub-compartments of the environment. It differentiates 19 impact categories at midpoint level and 34 impact categories at damage level. For more information on IW+, refer to our <a href="https://www.impactworldplus.org/">website</a> and <a href="https://doi.org/10.1007/s11367-019-01583-0">scientific article</a>. For information on the updates of IMPACT World+, you can register to the <a href="http://eepurl.com/dEeeJL">newsletter</a> of CIRAIG.</p> <p>The v2.1 update is the biggest update of the IMPACT World+ method in many years as it introduces new impact categories and updates many models with the latest available research.</p> <p>IMPACT World+ comes in three interpretation levels: <em><strong>midpoint</strong></em>, <em><strong>expert</strong></em> and <em><strong>footprint</strong></em>. You can find explanations for these three interpretation levels <a href="https://www.impactworldplus.org/version-2-0-1/">here</a>.</p> <p>The <em><strong>expert</strong></em> and <em><strong>midpoint</strong></em> versions of IMPACT World+ also come with two different implementations regarding how to account for <strong>biogenic carbon</strong>. One with the traditional biogenic <em>carbon neutrality approach</em> (e.g., where biogenic carbon dioxide is set at 0 and biogenic methane is set at 27kgCO2eq for GWP100) and one including the uptake of biogenic carbon dioxide, where the release of biogenic carbon is therefore set at the same CFs as fossil carbon, but the uptake is with a negative sign, i.e., a <em>-1/+1 approach</em> for biogenic carbon (look for the files marked “(incl. CO2 uptake)”).</p> <p>While we provide the -<em>/+1 approach</em>, we must make it clear to the users that this approach is heavily dependent on the quality of the inventory you are using, and that there are still <strong>issues </strong>currently with the LCI databases (even in ecoinvent 3.10). Furthermore, if you are using this approach, you either <strong>MUST </strong>adopt a <em>cradle-to-grave </em>approach to both account for the uptake and release of biogenic carbon (otherwise you will only account for the uptake of the carbon and have skewed results) or if you adopt a <em>cradle-to-gate</em> approach because you need to provide results to someone downstream of your supply chain you <strong>MUST </strong>communicate with that downstream user to tell them that they should account for the release of biogenic carbon in a <em>-1/+1 approach</em> also, otherwise, you and your downstream user will <strong>double count the benefits</strong> of using biogenic products, which is incorrect. This is especially true is the case of food products where the carbon emissions post consumption are typically not included in the inventories, which could result in a substantial under estimation of the impacts of this product over its life cycle.</p> <h3>Description of the files</h3> <p>- The dev file is a file useful for developers and maintainers of databases/datasets who wish to link IW+ 2.1 to their databases/datasets. It regroups all existing characterization factors of the IW+ LCIA method in an Excel format, using the terminology of IW+.</p> <p>- The "ecoinvent" files are Excel files matching with "pure" ecoinvent and its flow name terminology (as in unaltered by various software) in an Excel table. This is useful if you are using ecoinvent outside of LCA software.</p> <p>- The exiobase file links IW+ to the <a href="https://doi.org/10.5281/zenodo.5589597">Exiobase GMRIO database</a>. Once the file is read through pandas (pandas.read_excel()), the resulting matrix can directly be multiplied to the environmental extensions of exiobase (S, F or F_Y if using the pymrio package).</p> <p>- The openLCA file can be directly imported in the openLCA software as a JSON-LD file.</p> <p>- The SimaPro file can be directly imported in the SimaPro software as a CSV file.</p> <p>- The brightway2 files are ecoinvent-version dependent, so you need to select the correct file to work with the correct version of ecoinvent. Else, some of the ecoinvent flows which name did change in between versions of ecoinvent would not be characterized, leading to underestimated results. To import a file, you need to pass through brightway2 itself (it cannot be done through the activity-browser for now). The function to import a .bw2package file is bw2.BW2Package.import_file().</p> <p>- Finally, the source file regroups all the native information used by IW+ to derive the characterization factors. This file is primarily useful for the IW+ internal team. It is provided for transparency, as well as for curious users or users who wish to generate all these files themselves through the <a href="https://github.com/CIRAIG/IWP_Reborn">open-access code of IW+</a>.</p> <h3>New indicators</h3> <p>- <em>Plastic physical effect on biota</em></p> <p>This new indicator measures the effect of plastic resins emitted in the water environments (both freshwater and marine) on biota, in PDF.m2.yr. It also comes with a midpoint indicator in CTUe. This indicator is the result of the work of the <a href="https://marilca.org/characterization-factors/">MariLCA working group</a>.</p> <p>- <em>Fisheries impact</em></p> <p>This new indicator measures the impact on biodiversity of fisheries activities. It is only assessed at the ecosystem quality damage level (in PDF.m2.yr). This is based on the work of <a href="https://doi.org/10.3390/su16093870">Stanford-Clark et al.</a></p> <p>- <em>Marine ecotoxicity</em></p> <p>In the v2.1 we decided to finally integrate these two ecotoxicity indicators, at the damage level only. These are based on an old version of Usetox (v2.02). As the v3 of Usetox is on the verge of being released, all ecotoxicity and toxicity categories will be updated in the next version of IW+.</p> <p>- <em>Terrestrial ecotoxicity</em></p> <p>In the v2.1 we decided to finally integrate these two ecotoxicity indicators, at the damage level only. These are based on an old version of Usetox (v2.02). As the v3 of Usetox is on the verge of being released, all ecotoxicity and toxicity categories will be updated in the next version of IW+.</p> <p>- <em>Photochemical ozone formation</em></p> <p>For this impact category, IW+ adopts what the ReCiPe methodology recommends. In their <a href="https://doi.org/10.1007/s11367-016-1246-y">2016 update</a>, ReCiPe renamed the indicator "Photochemical oxidant formation" to "Photochemical ozone formation". In addition, they calculated the impact of this category on ecosystem quality. There are thus two corresponding impact categories at damage level: "Photochemical ozone formation, human health" and "Photochemical ozone formation, ecosystem quality"</p> <h3>Updated indicators</h3> <p>- <em>All climate change indicators</em></p> <p>IMPACT World+ v2.1 proposes the carbon neutrality approach (i.e., CO2-bio = 0) as well as the -1/+1 approach (CO2-bio uptake = -1 / CO2-bio release = +1). However, the latter is only available in the expert and midpoint versions. In the footprint version, the carbon neutrality assumption is still being used.</p> <p>Furthermore, we added CFs for temporary storage of biogenic carbon that can be used (e.g., Correction for delayed emissions, carbon dioxide, biogenic).</p> <p>- <em>Climate change, human health</em></p> <p>In the v2.0.1, we updated the GWP100 and GTP100 indicators following the recommendations of the AR6 from the IPCC2021. Now in the v2.1, we are also updating our damage indicators for climate change to follow the AR6 recommendations. Notably, the cumulative AGTP500 used in the derivation of these CFs was recalculated with updated equations (which we obtained thanks to Yue He and Thomas Gasser from the International Institute for Applied Systems Analysis - IIASA). In addition, the effect factors were also updated. Previously it was based on data from the World Health Organization from 2003, it is now based on the WHO 2014 report as well as the <a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">work of L</a><a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">. </a><a href="https://backend.orbit.dtu.dk/ws/portalfiles/portal/329521472/PhD_Thesis_Lea_Rupcic.pdf">Rupcic</a>.</p> <p>- <em>Climate change, ecosystem quality</em></p> <p>Similarly to the human health indicator the cumulative AGTP500 were recalculated. However, the effect factor was not updated yet for this impact category.</p> <p>- <em>Particulate matter formation</em></p> <p>Those CFs were updated to the latest model from Fantke, et al. This is composed of a series of articles on updates to <a href="https://doi.org/10.1021/acs.est.7b02589">fate</a> and <a href="https://doi.org/10.1021/acs.est.9b01800">effect</a> factors</p> <p>This model now provides regionalized characterization factors per town of more than 100,000 inhabitants. The CFs at the town-level are available in the source file, but in the dev file and in the different software versions, we only provide national/regional (e.g., RER) as well as global values, aggregated from the town-level factors.</p> <p>- <em>Water availability, human health</em></p> <p>Those CFs were updated to the latest model of L. Debarre (2024) [<em>publication in review, link will be added once published</em>]. This model includes a harmonization of methodology between the domestic and agriculture water use, updates the exposition factors using the latest Gross National Income data and updates the EF. Overall, the values of the characterization factors of this category have dramatically decreased, by a minimum of 65%.</p> <p>- <em>Water availability, terrestrial ecosystems</em></p> <p>While the original value of the characterization was not updated (e.g., 0.21 PDF.m2.yr in Netherlands), the regionalization was updated based on an estimation of depths of groundwater, based on <a href="https://doi.org/10.1126/science.abc2755">Jasechko (2021)</a>.</p> <p>- <em>Water scarcity</em></p> <p>Those CFs were updated to the latest update of the AWARE model Seitfudem (2024) [<em>publication in review, link will be added once published</em>].</p> <p>- <em>Fossil and nuclear energy use</em></p> <p>The HHV values were updated to match the updated HHVs in ecoinvent.</p> <p>- <em>Ozone layer depletion</em></p> <p>Those CFs were adapted to match the latest data from the <a href="https://ozone.unep.org/sites/default/files/2023-02/Scientific-Assessment-of-Ozone-Depletion-2022.pdf">World Meteorological Organization (2022)</a>. In addition, the time horizon has now been extended to the infinite instead of limiting it to 500 years.</p> <h3>Methodology</h3> <p>For more detail on the methodology behind each impact category, refer to our <a href="https://github.com/CIRAIG/IWP_Reborn/tree/master/Methodology">Github</a>, in the future it will be available directly on our website.</p> <h3>Corrections</h3> <p>In this section we only provide information on the major corrections that were made. For a full report of all the changes please refer to out <a href="https://github.com/CIRAIG/IWP_Reborn/tree/master/Report_changes">Github</a>.</p> <p>- There are challenges associated with using IMPACT World+ files across databases or software for which they were not specifically designed. For instance, this is why we now provide files adapted to particular ecoinvent versions in brightway2. Similarly, both SimaPro and openLCA periodically update the names of their elementary flows. When an impact assessment method has previously been imported into one of these tools, an embedded procedure in their update processes is supposed to adjust the characterization factor names in line with the new flow names, ensuring compatibility with the updated list. However, we lack detailed knowledge of this procedure, meaning we cannot guarantee that the updated versions of IW+ in these tools would align with our specific modeling choices. Likewise, the IW+ versions we provide for a given release of SimaPro or openLCA might be incompatible with previous or subsequent versions due to discrepancies in flow names and characterization factors. Consequently, users of these software should verify which flows are characterized and make adjustments if necessary.</p> <p>- Harmonization of regionalized flows</p> <p>Regionalized impact model do not operate at the same geographical granularity. In the previous version, some flows were characterized in one impact category but not in the other. For instance, the flow "Water, lake, US-TRE" was characterized for the "water scarcity" indicator but not for "Water availability, human health". All regionalized flows are now characterized for all the impact categories they affect. This is also true for the newest regionalized impact category (Particulate matter formation) where SO<sub>2</sub> for example is regionalized at a much more granular level than in the freshwater acidification impact category.</p> <p>- Fossil and nuclear energy use</p> <p>For the SimaPro version of the v2.0.1, some flows that only exist in SimaPro were not characterized, such as "Oil, crude, 43.4 MJ per kg". Now they are properly characterized using the energy content value specified in the name. Results obtained with SimaPro will thus differ from results obtained with brightway2 and openLCA, since the latter do not use such flows and only use flow such as "Oil, crude".</p> <p>- Thermally polluted water</p> <p>In the v2.0.1, the flows "Water, turbine use, unspecified natural origin" were not linked to the correct proxy, which meant they did not impact the Thermally polluted water category, which underestimated the impact of this category.</p> <p>- The problem of "Nitrogen"</p> <p>"Nitrogen" can mean two different things. It can literally mean the "N" element, but it can also mean the "N2" molecule. The issue is that it is not clear in the LCI databases, which meaning does "Nitrogen" have. Previously, our understanding was that "Nitrogen" meant "N" but since then, ecoinvent notably, added formulas to the elementary flows they provide and associated the formula "N2" to their "Nitrogen" flows, indicating that they understand it as "N2" and not "N". Furthermore, in SimaPro and openLCA, the associated CAS number is generally "7727-37-9" which again corresponds to "N2". Thus, we now consider that “Nitrogen” represents dinitrogen, and thus does not impact the "Marine eutrophication" impact category anymore. We also corrected a previous mistake: N<sub>2</sub>O is not characterized anymore for this impact category.</p>
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
Use of geolocators for investigating breeding ecology of a rock crevice-nesting seabird: method validation and impact assessment
<p>1: Investigating ecology of marine animals, imposes a continuous challenge due to their temporal and/or spatial unavailability. Light-based geolocators (GLS) are animal-borne devices that provide relatively cheap and efficient method to track seabird movement and are commonly used to study migration. Here we explore the potential of GLS data to establish individual behaviour during the breeding period in a rock crevice-nesting seabird, the Little Auk, Alle alle. 2: By deploying GLS on 12 breeding pairs, we developed a methodological workflow to extract birds' behaviour from GLS data (nest attendance, colony attendance and foraging activity), and validated its accuracy using behaviour extracted from a well-established method based on video recordings. We also compared breeding outcome, as well as behavioural patterns of logged individuals with a control group treated similarly in all aspects except for the deployment of a logger, to assess short-term logger effects on fitness and behaviour. 3: We found a high accuracy of GLS-established behavioural patterns, especially during the incubation and early chick rearing period (when birds spend relatively long time in the nest). We observed no apparent effect of logger deployment on breeding outcome of logged pairs, but recorded some behavioural changes in logged individuals (longer incubation bouts and shorter foraging trips). 4: Our study provides a useful framework for establishing behavioural patterns (nest attendance and foraging) of a crevice-nesting seabird from GLS data (light and conductivity), especially during incubation and early chick rearing period. Given that GLS deployment does not seem to affect the breeding outcome of logged individuals but does affect fine-scale behaviour, our framework is likely to be applicable to a variety of crevice/burrow nesting seabirds, even though precautions should be taken to reduce deployment effect. Finally, because each species may have its own behavioural and ecological specificity, we recommend performing a pilot study before implementing the method in a new study system.</p>
Protecting the resource: an assessment of mitigation methods used to protect large trees from African elephant impact in a savanna system
<p>African elephants (<em>Loxodonta africana</em>) can alter the structural components of savanna ecosystems, often through the reduction of the large tree (>5 m height) cover component. Elephant impact can be amplified in small, protected areas, or areas where water is readily available to elephants. One management option is to protect large trees directly using applied mitigation methods to limit elephant impact. In this paper, we assessed and compared the effectiveness and logistical requirements of four mitigation methods that have been applied to protect large trees from elephant impact in South Africa's Greater Kruger National Park - namely African honeybees (<em>Apis mellifera scutellata</em>) in beehives; creosote oil in glass jars, concrete pyramids arranged in circles around trees, as well as wire-netting the trees' main stems. For each method, elephant impact levels and tree mortality rates were measured over a 2–5-year period depending on the method in use. Sample sizes ranged from 43 to 59 trees per mitigation method, with a comparable control, which was a tree of the same species and morphological dimensions but lacking any mitigation application. Beehives were the most effective method at reducing tree loss, significantly reducing tree mortality from 34% (6.8%/year) in control trees to only 10% (2%/year) over the five-year experimental period. However, beehives were the most expensive method to apply to a tree, although this cost can be compensated through honey sales. Concrete pyramids reduced tree loss when the combined pyramid radius was >1.5 m in length, whilst wire-netting was effective against bark-stripping by elephants but was still vulnerable to heavier forms of impact such as uprooting and stem snapping. Creosote jars did not prevent elephants from impacting treated trees. Our results provide managers with a toolkit for protecting large trees against elephant impact, commenting on both the efficacy and the logistical constraints for each method.</p>
Protecting the resource: an assessment of mitigation methods used to protect large trees from African elephant impact in a savanna system
Open the record for dataset details and reuse information.
Use of geolocators for investigating breeding ecology of a rock crevice-nesting seabird: method validation and impact assessment
Open the record for dataset details and reuse information.
Data supporting 'Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealized Aquaplanet GCM' by Neil T Lewis et al.
<p>Data supporting Lewis et al., 2024. Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealised Aquaplanet GCM. Submitted to Journal of Climate. </p> <p>All data is in NetCDF format. </p> <p>Output from each experiment is contained in its own folder (e.g., 'ALB.1'). For a description of each experiment, see the accompanying paper. </p> <p>Data files contain the following outputs: </p> <p>dyn_vars_daily_clim.nc contains day of year- and zonally-averaged atmospheric fields (u, v, T, etc). </p> <p>eddy_products.nc constains day of year- and zonally-averaged products of atmospheric fields (e.g., u'v'). </p> <p>ice_temp_daily.nc contains daily-averaged surface temperature and sea-ice thickness. </p> <p>toa_fluxes_clim.nc contains day of year-averaged top of atmosphere radiative fluxes (e.g., OLR). </p> <p>For the experiments NDG1.05, NDG1.1, and NDG1.2, nudge_daily_clim.nc is also included, and contains the day of year-averaged nudging heat flux applied to melt the ice. </p>
Assessment of the Relationship Between Edema Measurement Methods After Impacted Mandibular Third Molar Surgery.
ClinicalTrials.gov study NCT03747237. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing the Impact of a Change to the Work Schedule of Resident Physicians: a Mixed Methods Study
ClinicalTrials.gov study NCT01398878. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing the Impact of Cell Isolation Method on B cell Gene Expression using Next-Generation Sequencing
GEO Series GSE279633. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Assessing the Impact of Two Methods of Continuous Veno-venous Hemodiafiltration on Time Nursing Work in Intensive Care
ClinicalTrials.gov study NCT00993733. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
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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.