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16,255 results for “risk”
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
Lead Risk Factors for West and North Philadelphia: 2007-2020
Provides all data used for the creation of the maps and spearman correlation analysis for a lead risk assessment of North and West Philadelphia. This includes data on: lead-in-soil, land recycled sites, demolitions, housing code violations, age of housing, smelters, and elevated blood lead levels of children. The data was used for a spatial analysis of historic and current lead sources in West and North Philadelphia to determine which lead sources act as primary lead-risk factors, identify future soil sampling sites and high-risk neighborhoods in Philadelphia.
A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)
<p>The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.</p>
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
Inter-Chemical Correlation results for the study: HHEARx2017-1945 (Maternal and Developmental Risks from Environmental and Social Stressors (MADRES))
Title: Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) <br>Species: Homo sapiens <br>Number of samples: 421 <br>Number of named analytes: 21 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=32 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1432 (Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children)
Title: Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children <br>Species: Homo sapiens <br>Number of samples: 1256 <br>Number of named analytes: 51 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=5 <br>
A comparative dataset on public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden
<p>These datasets are the result of two nation-wide surveys conducted in Italy and Sweden in August 2020 and in november 2020. The surveys (which are identical in the two rounds) explore the respondents' risk perception, preparedness, knowledge, and experience regarding a set of hazards, namely: epidemics, floods, droughts, earthquakes, wildfires, terror attacks, domestic violence, economic crises, and climate change. </p> <p>The data files include the questionnaire survey (the Italian and Swedish versions as well as the English translation) and the two datasets of all the answers to the two surveys. Each column in the dataset refers to an item in the survey (e.g. a question or a sub-question), and each row represents a single respondent. </p> <p>For additional information on the August 2020 dataset, see <a href="https://www.nature.com/articles/s41597-020-00778-7">Mondino et al. (2020)</a>.</p>
Dataset of "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"
<p>The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings identified TWIST1 as novel therapeutic target in NB.</p>
Dataset for "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"
<p>The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings confirm TWIST1 as promising therapeutic target in NB.</p> <p>This dataset comprise images (.ndpi files) of anti-F4/80 IHC staining used for the quantification of macrophages in subcutaneous and orthotopic neuroblastoma xenografts derived from SK-N-Be2c cells expressing TWIST1 or knocked out for TWIST1 through CRISR/Cas9.</p>
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).
<p>This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf.</p> <p>Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1).</p> <p>The data is described in more detail in paragraph 4.1 (table 4.2).</p> <p>* fork1_all.csv: MySQL server / MariaDB server<br> * fork2_all.csv: Linux kernel / Android kernel<br> * fork3_all.csv: Apache OpenOffice / LibreOffice</p> <p>==Cite==<br> [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented<br> by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292</p>
Health of the Nation. National NCD risk factor survey, Barbados (2012-13)
<p><strong>Executive Summary</strong></p> <div> <div>The Caribbean is experiencing increasing levels of illness and death from non-communicable disease (NCD) causes. Regional leaders pledged to combat this epidemic through increased surveillance and intervention, implementing healthcare policies and programmes across our countries. In Barbados, one of the Ministry of Health (MoH)’s initiatives has been the Health of the Nation (HotN) Survey, to provide information on the prevalence and social determinants of risk factors for lifestyle-related NCD. This will allow identification of potential targets for future interventions to improve prevention and control of these diseases in the Barbadian population. In this comprehensive, cross-sectional survey, data were collected for 1234 participants aged at least 25 years (response rate: 55%) on demographics, behavioural risk factors, medical history, place of treatment and costs incurred, blood pressure and anthropometry, and biochemical measures. The survey sample under-represented young adults (particularly men) and over-represented the elderly (particularly women), so a weighting scheme was utilised to balance the sample distribution for age and sex with that of the Barbados 2010 Census. Prevalence of each risk factor was estimated overall, for each sex separately, and stratified by three broad age-groups.</div> <br> <div>Main findings show that Barbadian adults are at high risk from NCDs due to high prevalence of biological and behavioural risk factors. Most alarming is that two in every three adults in our population (and three-quarters of women) are overweight and/or obese. In addition, more than one in three adults in Barbados (more than one in two of those aged at least 45 years) are hypertensive, and one in five have diabetes (almost one in two of those aged 65 years or older). At least one in three of those with known hypertension or diabetes who were receiving treatment had sub-optimal control. </div> <br> <div>Daily tobacco use was reported by one in 10 men, vs one in 50 women. Harmful alcohol use followed a similar pattern, i.e. was mainly reported by young men, with excessive weekly alcohol consumption over the past 30 days reported by roughly the same proportions of men and women reporting daily tobacco use. One in three men aged 25–44 years reported binge drinking in the past 30 days. Core survey results show that Barbadian residents have low fruit and vegetable consumption, while half of the sample reported low levels of physical activity. About one in four adults had healthcare insurance (one in three of those who were employed). More in-depth information on diet, physical activity and cost/insurance will be provided from the relevant survey sub-studies at a later date. Urgent action is required to address the low levels of healthy behavioural risk and high levels of biological risk present in the Barbadian adult population. Community and civil society involvement could help support healthier behaviours. A multi-sectoral approach is required to combat NCD risk on all levels, with creation of national guidelines to supplement an appropriate regulatory framework within an enabling environment.</div> </div>
COVID19 Flow-Maps Mobility-Associated-Risk
<p><strong>The Mobility Associated Risk</strong></p> <p>The Mobility Associated Risk is a risk score combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard The Mobility Associated Risk combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard <a href="https://flowmaps.life.bsc.es/flowboard/">https://flowmaps.life.bsc.es/flowboard/</a></p>
Dataset with Risk estimates of major currency pairs on the Forex market
<p>This dataset includes Value at Risk (VaR) and Expected Shortfall (ES) estimations of the major currency pairs on the Forex market. Notably, it provides daily VaR and ES estimates for the AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, and USDJPY FX assets for January 2021 to September 2022. The reported risk estimates were calculated by various parametric and non-parametric models, including Variance-Covariance (VS), Historical Simulation (HS), Monte Carlo (MC), and Garch(1,1) at both 95% and 99% confidence levels. To enable model evaluation, the last column of each CSV file, named pnl, provides the actual daily returns of the FX asset.</p> <p>The data and code used to create this dataset are available at <a href="https://doi.org/10.5281/zenodo.7411148">Zenodo</a> and <a href="https://marketplace.infinitech-h2020.eu/assets/portfolio-value-at-risk-estimation">INFINITECH Marketplace</a>, respectively.</p>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials
<p><strong>Abstract</strong></p> <p>Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML.</p> <p> </p> <p><strong>Methods</strong></p> <p>The upload contains two zip files and a .json file.</p> <ul> <li>plain.html.zip</li> </ul> <p>Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. </p> <p> </p> <ul> <li>ann.json.zip</li> </ul> <p>The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. </p> <ol> <li>annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. </li> <li>metas (object): full-text document label</li> <li>entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to.</li> <li>relations (array)</li> <li>sources (array)</li> </ol> <p> </p> <ul> <li>annotations-legend.json</li> </ul> <p>This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113".</p> <p> </p> <p><strong>Resources</strong></p> <p>The code to parse annotations can be found on <a href="http:// https://github.com/anjani-dhrangadhariya">GitHub</a>.</p> <p> </p> <p><strong>Funding</strong></p> <p>HES-SO Valais-Wallis, Sierre, Switzerland</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.