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Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.
<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1708">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1707">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
CESM FKESSLER input data for running CESM in docker container
This dataset contains input data for running CESM 2.1.1 with FKESSLER compset and resolution T31_g37. <pre>create_newcase --case /home/cesm/cases/fkessler --compset FKESSLER \ --res T31_g37 --compset FKESSLER --machine espresso --run-unsupported </pre>
CESM F1850 input data for running CESM in docker container
<p>This dataset contains input data for running CESM 2.1.1 with F1850 compset and resolution f09_g17.</p> <p> </p> <pre>create_newcase --case /home/cesm/cases/B1850 --compset B1850 \ --res f09_g17 --machine espresso --run-unsupported && \ cd /home/cesm/cases/B1850 </pre> <p>The case uses <a href="https://bioconda.github.io/recipes/cesm/README.html">cesm from bioconda</a>.</p>
CESM input data for running CESM historic with CAM6 and CLM5 (no ocean) in docker container
<p>CESM docker container for HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV compset and resolution f19_g17 using <a href="https://bioconda.github.io/recipes/cesm/README.html">bioconda cesm docker</a> as a base image.</p>
A linearized Navier-Stokes input output model
<p>Matrices for the experiments reported in DOI:10.1137/140980016</p>
Aurora Subglacial Basin GlaDs inputs, outputs and geophysical data
<p>The GlaDS_ASB_outputs.txt file includes the following:</p> <p>Glacier Drainage System (GlaDS) model inputs: node easting (m), node northing (m), bed elevation (m), ice thickness (m), basal velocity (m/year) and basal water production (m/year). GlaDS model results for water pressure as a fraction of overburden (Pw/Pi) and water depth (m):base line model, high conductivity, low conductivity, static water and static velocity model runs. </p> <p>Specularity content data for Aurora Subglacial Basin as an xyz file called: filtered.spec.asb.xyz with easting (m), northing (m) and specularity content. </p> <p>The ICECAP basal interface specularity content profiles can also be found at the U.S Antarctic Program (USAP) Data Center: <a href="https://doi.org/10.15784/601371">https://doi.org/10.15784/601371</a></p>
Figure. Lateral view and mouth shape of (A) Capoeta damascina, NUIC-1519, 158.9 mm SL; Malatya prov.: Sürgü Stream. TigrisEuphrates basin (B) C. damascina, NUIC-1520, 163.5 mm SL; Gaziantep prov.: Merzimen Stream, Tigris-Euphrates basin (C) C. damascina, NUIC-1521, 152.3 mm SL; Adıyaman prov.: Input of Atatürk Dam Lake, Tigris-Euphrates basin (D) C. damascina, NUIC-1817, 127.3 mm SL; Kilis prov.: Sapkanlı Pond, Orontes basin (E) C. kosswigi, NUIC-1907, 179.3 mm SL; Van prov.: Karasu Stream, Lake Van basin (All from Turkey). in Capoeta kosswigi Karaman, 1969 a junior synonym of Capoeta damascina (Valenciennes, 1842) (Teleostei: Cyprinidae)
Figure. Lateral view and mouth shape of (A) Capoeta damascina, NUIC-1519, 158.9 mm SL; Malatya prov.: Sürgü Stream. TigrisEuphrates basin (B) C. damascina, NUIC-1520, 163.5 mm SL; Gaziantep prov.: Merzimen Stream, Tigris-Euphrates basin (C) C. damascina, NUIC-1521, 152.3 mm SL; Adıyaman prov.: Input of Atatürk Dam Lake, Tigris-Euphrates basin (D) C. damascina, NUIC-1817, 127.3 mm SL; Kilis prov.: Sapkanlı Pond, Orontes basin (E) C. kosswigi, NUIC-1907, 179.3 mm SL; Van prov.: Karasu Stream, Lake Van basin (All from Turkey).
Annual crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of United States from 1850 to 2022: Application rate, timing, and method
<p>This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024).</p> <p>This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop.</p> <p>A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion. </p> <p>This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036).</p> <p> </p>
SeisSol model setup input files and supplement videos for the 3D dynamic rupture models of Wirp et al. 2024
<p>Data required to run the dynamic rupture models presented in Wirp, S. A., Gabriel, A.-A., Ulrich, T., Lorito, S. (2024). The README.txt file contains detailed information about the data and data format.</p>
Can green hydrogen drive economic transformation in Saudi Arabia? - An input-output analysis of different Power-to-X configurations. Supplementary Data
<p>Supplementary material for peer review</p> <ul> <li>Modelling Data (input & results)</li> <li>Literature Review</li> </ul>
Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning
<p>Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning</p> <p>Data contains shapefiles that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the <em>JOURNAL NAME</em> publication and the Zenodo dataset.</strong></p> <p>Iqbal, W., Head III, J. W., van der Bogert, C. H., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Slopes along Apollo EVAs: Astronaut experience as input for future mission planning. Acta Astronautica, 223, 184-196. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actaastro.2024.07.006" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.actaastro.2024.07.006</a></p> <p>Iqbal, W., Head, J. W., van der Bogert, C., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning [Data set]. In Acta Astronautica (Bd. 223, S. 184–196). Zenodo. <a href="https://doi.org/10.5281/zenodo.13790204" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13790204</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Structure</p> <p>-> File "Apollo_Traverses_Iqbal_24" - It includes six subfolders for each landing site that contain the shapefiles of traverses.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Example input and output for RSYD-BASIC pipeline
<p><strong>Input data:</strong></p> <ul> <li>20230914-data-overview.xlsx: mapping of placeholder sample numbers to original SRA accession numbers</li> <li>1199234567-*.fastq.gz: renamed Illumina MiSeq reads; reads for 1199234567-5 have been obtained by downsampling SRR10955980</li> <li>madsdata: LIS report and bacteria list used for LIS-specific results</li> </ul> <p><strong>Expected results:</strong></p> <ul> <li>Results of the RSYD-BASIC pipeline, version 1.15.1, with the test dataset</li> </ul>
Low-input breeding in stone pine, a multipurpose forest tree with low genome diversity
<p><span>Stone pine (<em>Pinus pinea</em> L.) is an emblematic tree species within the Mediterranean basin, with high ecological and economic relevance due to the production of edible nuts. Breeding programmes to improve pine nut production started decades ago in Southern Europe but have been hindered by the near absence of polymorphisms in the species genome and the lack of suitable genomic tools. In this study, we assessed new stone pine’s genomic resources and their utilisation in breeding and sustainable use, by using a commercial SNP-array (5,671 SNPs). Firstly, we confirmed the accurate clonal identification and identity check of 99 clones from the Spanish breeding programme. Secondly, we successfully estimated genomic relationships in</span><span> clonal collections, an information needed for </span><span>low-input breeding and genomic prediction. Thirdly, we applied this information to genomic prediction for total number of cones unspoiled by pests and their weight measured in three Spanish clonal tests. Genomic prediction accuracy depends on the trait under consideration and possibly on the number of genotypes included in the test. Predictive ability (<em>r</em><sub><span>y</span></sub>) was significant for the mean cone weight measured in the three clonal tests, while solely significant for the number of cones in one clonal test. The combination of a new SNP-array together with the phenotyping of relevant commercial traits into genomic prediction models, proved to be very promising to identify superior clones for cone weight. This approach opens new perspectives for early selection. </span></p>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
FRACTESUS_UC_S690_T0_MCT_input
<div>Fractesus project. Fracture test mini-CT. Master curve input S690Q. UC. </div> <div> <div> </div> </div>
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.